As the deadly Bundibugyo strain of Ebola continues to ravage parts of Central Africa, physicians once again find themselves scrambling for ways to keep the sickest patients alive.

Existing antibody treatments are strain-specific and don’t target the virus responsible for the current outbreak, leaving few therapies capable of clearing virus from the bloodstream. This forces doctors to rely largely on supportive care for people in advanced stages of disease.

That treatment gap is reviving interest in experimental blood-filtering devices that can physically remove viral particles from the bloodstream.

These systems have been studied primarily as treatments for cancer, where they help remove tumor-derived particles, and in more common infectious diseases such as COVID-19 and hepatitis C. However, one such device was successfully deployed during the last major outbreak of Ebola, helping to drive down exceedingly high viral levels in one critically ill patient.

If the current outbreak expands further, as some infectious-disease experts warn it could, the same technology may once again be called into action—not just as a desperate last-resort intervention for a single patient, but as a potential tool for keeping more Ebola patients alive.

“It could really help,” says Stefan Büttner, a nephrologist and intensive-care specialist at the Klinikum Aschaffenburg-Alzenau in Germany.

Filter Removes Millions of Viral Particles

This year’s Ebola outbreak, though serious, is nowhere near the scale of the catastrophic epidemic that started in late 2013 and persisted for nearly 2.5 years.

Back then, there were more than 28,000 confirmed cases and 11,000 deaths—mostly in the West African nations of Sierra Leone, Liberia, and Guinea, though the virus spread in nearby countries as well. Today, the toll is far smaller: roughly 1,000 suspected cases and fewer than 300 related deaths, all concentrated in the eastern Democratic Republic of Congo, with limited spillover into Uganda.

Still, the emergence of the Bundibugyo strain, coupled with the lack of approved therapies designed to target it, has raised fears that doctors could once again find themselves without effective tools if the virus spreads further.

The situation was similar in 2014, before the development of monoclonal antibody therapies that dramatically improved survival against the more common Zaire strain of Ebola. So, when a Ugandan doctor—infected with Ebola while treating patients in Sierra Leone—was medevacked to Germany in critical condition, the ICU team at Frankfurt University Hospital, which included Büttner at the time, tried nearly everything they could.

Nothing seemed to halt the disease’s progression. The man’s condition only got worse. His organs began to shut down.

Then, with emergency approval from German regulators, Büttner and his colleagues connected the patient to the Hemopurifier, a baton-size cartridge filled with sticky proteins from the common snowdrop plant. These proteins, like a kind of molecular Velcro, latch onto sugar molecules that coat viruses like Ebola and trap them as blood passes through the system.

In this illustration, a zoomed-in view of the Hemopurifier shows how it traps the Ebola virus by passing blood through tiny fibers coated with sticky proteins.Aethlon Medical

The Hemopurifier itself is not electrical. Instead, it connects inline to an intensive-care-grade dialysis machine, the artificial-kidney-like device that pumps the patient’s blood through its own filter to strip out toxins and surplus fluid before returning it. The Hemopurifier rides on that same circuit, and on that same machine’s electronics. The dialysis unit’s pumps push the blood through the cartridge, while its sensors balance fluid, watch circuit pressures for safety, and automatically meter the anticoagulant that keeps the blood from clotting along the way.

Though he had been on emergency dialysis for days, the Ugandan doctor had the Hemopurifier added into the circuitry for just 6.5 hours. His blood sloshed through the device’s tiny channels and pressed against its protein snares. By the end of the brief treatment, the device had captured a whopping 253 million copies of the Ebola virus, and the man’s situation quickly turned around.

His viral load dropped from around 380,000 particles per milliliter of blood before the procedure to roughly 6,000 the next day. His immune system, no longer overwhelmed by runaway viral replication, then regained the upper hand and finished the virus off on its own.

Less than a week after the treatment, as Büttner’s team reported in 2015 in the journal Blood Purification, the patient was Ebola-free.

The Technology Is Ready to Deploy

Though it is impossible to know how much of the Ugandan doctor’s recovery could be attributed to blood filtration, Büttner believes the treatment played an important role. And he is confident the approach could prove even more beneficial for patients with much higher viral loads, who might not otherwise survive, while also helping to limit the organ damage and other complications that often arise during prolonged stays in intensive care.

“Earlier is better,” Büttner says.

Should the need to test that idea emerge during the current outbreak, Aethlon Medical, the company behind the Hemopurifier system, says it is prepared to move quickly.

Back in 2014, the company secured FDA authorization for a compassionate-use protocol allowing the Hemopurifier to be used in up to 20 patients with Ebola across 10 clinical sites in the United States. More than a decade later, authorization remains active and available for use, according to the company. “That avenue is still open,” says chief medical officer Steven LaRosa.

And although the device has never been evaluated against the Bundibugyo strain, LaRosa says its mode of action suggests it should work regardless of Ebola subtype. Given Büttner’s experience treating the man infected with the Zaire strain, together with laboratory studies demonstrating capture of the related Marburg virus, he expects the Hemopurifier would be able to filter Bundibugyo virus as well.

“I have confidence that it would likely be removed,” LaRosa says.

For proponents of blood filtration, the major obstacle is therefore not technological. The devices already exist, can be integrated into standard dialysis and critical-care equipment, and appear capable of capturing a broad range of pathogens, Ebola included.

The harder challenge, they say, is convincing physicians, regulators, and health systems to embrace a treatment paradigm built around physically extracting disease-causing agents rather than targeting them with pharmaceuticals. And even if that skepticism were to fade, major logistical challenges remain.

The Hemopurifier and other systems like it are designed to operate with dialysis-style blood-circulation systems that require specialized equipment, reliable power, trained personnel, and large-bore vascular catheters. Such resources are readily available for patients who can be evacuated to major European medical centers in places like Frankfurt. They are typically nonexistent in the austere settings where Ebola outbreaks most often occur.

What the field still needs is therefore a “ruggedized” version of the technology that can hold up outside the controlled environment of a hospital ICU, says Michael Super, an infectious-disease researcher at the Wyss Institute for Biologically Inspired Engineering at Harvard who has spent years developing his own blood-cleansing devices.

“That, from a practical point of view, could be something that’s very useful,” he says.

Designing for the Outbreak Zone

Lower-tech versions of blood-filtration systems are in development, and some medical-device makers have begun sketching out designs that could, in principle, operate without any hospital infrastructure—some even without electricity.

For example, patent filings from Stavro Medical, a company recently acquired by ExThera, describe a manual system in which a health care worker uses syringes to push blood through a filter cartridge in batches—or, alternatively, simply raises one reservoir above another so that blood flows downhill through the filter on its own.

Aethlon, for its part, is pursuing a more modest goal. According to LaRosa, the company is developing a stripped-down version of its Hemopurifier system that could run through a standard IV line rather than the thick catheter that dialysis often requires. “That’s not ready for prime time yet,” he says. “But we’re working on it.”

In the end, however, what may push blood filtration into the Ebola treatment tool kit is not an engineering advance but a body count. A spreading outbreak could hasten the climb from experimental footnote to front-line tool.

This is the place where you face yourself,
the you that could be you with a few
different parts, a pump for your heart,
eyes off color, and fresh off the shelf
fake hair (a bit obvious), skin smoothed.
You’re not perfect, but it’s a good start.

Down to small digits, you’ll be improved.
Memory maintained by small motors,
as long as these gizmos don’t glitch.
What’s before you? Full replacement or
a constant game of test and switch,
pieces peeled off, disconnected, removed,
until you are not yourself, at least,
not the self you knew. That self has ceased,
bit by bit less you at each release.

A new method for writing DNA promises to unlock the potential of generative AI in biology, giving scientists a fast, affordable, and accurate way to physically build the novel genetic sequences that predictive models are now producing faster than anyone can construct them.

The technique, called Sidewinder, can assemble dozens of genetic sequences simultaneously in a single test tube, producing just one incorrect junction for every 10 million assembly events—a level of precision that far surpasses conventional methods, which misfire roughly once every 10 to 30 joins. Sidewinder also draws on cheap raw materials that have until now been too difficult to use reliably.

“It’s a step change,” says Thomas Gorochowski, a bioengineer at the University of Bristol, in England, who was not involved in the research. “It really opens up the feasibility of synthesizing large genetic systems, maybe even small genomes.” And that, he adds, “is uber-important for all of the AI stuff that’s coming out at the moment around generative genome sequences.”

The advance, presented earlier this month at SynBioBeta 2026 in San Jose, Calif., and detailed in a preprint posted to bioRxiv, addresses one of the more vexing mismatches in modern genomics research. Generative AI tools like Evo 2, trained on the genetic code of millions of organisms, can design new DNA sequences on demand at extraordinary speed. But physically constructing long DNA sequences in a laboratory has remained slow and expensive, especially when building not just one sequence at a time but dozens of different designs simultaneously, as testing AI predictions at scale demands.

RELATED: Can Biologists Rewrite the Genome’s Spaghetti Code?

In a demonstration of how squarely Sidewinder targets this bottleneck, the team behind the technique, led by Caltech synthetic biologist Kaihang Wang, harnessed the power of Evo 2 to redesign a 12,500-letter DNA sequence of the E. coli genome in silico and then used Sidewinder to build it from scratch—with no errors. Sequences of that length can encode entire biochemical pathways, laying the groundwork for engineered microbes that manufacture drugs, biofuels, or specialty chemicals, and eventually to the assembly of vast DNA constructs approaching complete artificial genomes.

In the past, says Brian Hie, the Stanford computational biologist whose lab developed Evo 2, a project like this would likely take more than a month, based on his team’s experience with conventional commercial methods. “With a technology like this,” he says, “you could probably achieve the same thing in a few days.”

To commercialize Sidewinder, [from left] Noah Robinson, Kaihang Wang, Adrian Woolfson, and Brian Hie cofounded a company called Genyro. Marcus Ubungen

A New Assembly Logic

The new method builds on a DNA synthesis strategy that Wang and his colleagues first outlined at the beginning of the year in Nature, but with substantially greater capacity.

Thanks to a new algorithm that automates the most computationally demanding part of the process and laboratory innovations in how raw ingredients are managed, it is now feasible to synthesize ever larger and more numerous DNA constructs simultaneously. This opens up applications including drug discovery, data storage, and the design of synthetic organisms.

“The pace at which you can start to explore these things just opened up massively,” Gorochowski says.

To understand how Sidewinder works, it helps to understand how DNA is typically made in a laboratory. The process begins with short, chemically manufactured strands called oligonucleotides, or oligos, the molecular alphabet blocks from which longer sequences are assembled.

Ordering oligos individually is reliable but expensive. Scientists discovered years ago that they could slash costs by synthesizing thousands of different oligos together in a single pool. But doing so creates a chaotic soup in which fragments tangle with unintended partners, leading to errors.

Sorting out specific sequences from such a pool has traditionally required elaborate separation steps: physically dividing up the fragments, isolating them in tiny droplets, or fishing them out one by one with laser light. Each approach added cost, time, and specialized equipment.

The Caltech team sidestepped the problem entirely.

Page Numbers for DNA

Sidewinder also starts with oligos, the kind anyone can buy from DNA synthesis vendors such as GenScript or Twist Bioscience, but tags each fragment with a unique molecular barcode. This short identifying sequence ensures that each piece links up only with its intended neighbor in the order that will yield the desired genetic sequence. When two bar-coded fragments meet, they form what chemists call a three-way junction: a fleeting molecular knot that locks the pieces in alignment before being cleanly removed, leaving a seamless strand.

Wang likens these barcodes to page numbers. Whereas conventional assembly is like collating an unnumbered manuscript by matching the last line of one page to the first line of the next—workable for a short document, a recipe for chaos when sequences repeat—Sidewinder’s barcodes guide each fragment to its correct partner regardless of what sequence it carries.

The original Sidewinder protocol required a computationally intensive calculation to design those barcodes, however, and this became impractically slow as the number of fragments grew.

A former Caltech undergraduate student named Jean-Sebastien Paul developed a workaround. While working in Wang’s lab one summer, Paul, who is now pursuing a Ph.D. at Stanford, built a software tool called PyWinder that churns out the barcodes in minutes on a standard laptop, replacing a calculation that had previously been too slow to scale.

Bioengineer Noah Robinson, a postdoc in Wang’s lab who codeveloped the original Sidewinder method, also adapted the approach to work from cheap, mass-produced DNA ingredients, further cutting time and cost.

Wang and Robinson, together with Hie and entrepreneur Adrian Woolfson, cofounded a company called Genyro—to commercialize the technology, hoping to turn a profit through paying pharmaceutical and biotech clients. According to Robinson, however, they intend to make the Sidewinder platform broadly accessible to the academic research community.

“We really want this to be an enabling platform,” says Robinson. “We want people to do cool things with the technology.”

It can be difficult to carry on conversation in a crowded public setting, and even more so with any degree of hearing loss. But what if you could amplify only the person you wanted to hear and suppress the rest? What if a computer could do that automatically by reading your brain?

When we focus on a particular person talking, we subconsciously track the gradual modulations in speech volume, which vary from speaker to speaker. This characteristic pattern appears in the brain activity of the listener. And in recent years, researchers have been able to find the signature speech pattern in a brain recording, then identify the voice being listened to, using a technique called auditory attention decoding (AAD).

This fundamental neuroscience is now stepping into the realm of practical medicine. By testing the actual experience of listeners, researchers are taking a step toward one day incorporating attention-based control into hearing aids. In a study published 11 May in Nature Neuroscience, researchers presented listeners with two competing voices, then applied real-time volume adjustments in response to brain activity. The altered audio improved understanding, reduced listening effort, and was simply preferred by listeners.

RELATED: These Hearing Aids Will Tune In to Your Brain

The study serves as an important proof of principle. “It validates the core idea that brain-controlled hearing enhancement can improve perception, while also making clear what still needs to be solved before this could become practical for patients,” says Inyong Choi, an engineer and psychoacoustician at the University of Iowa, who was not involved in the research.

Amplifying the Voices You Want to Hear

According to the World Health Organization, more than 400 million people worldwide have disabling hearing loss. In the United States, roughly 15 percent of adults live with some form of hearing loss, often related to aging. Hearing deficits can have serious social and mental-health consequences. On 7 May, the Advanced Research Projects Agency for Health (ARPA-H) announced a new funding program for hearing aid research with stated goals including neural control or feedback.

Hearing aids are more technologically advanced than ever, says Bharath Chandrasekaran, a neuroscientist who studies hearing and the brain at Northwestern University in Chicago. But they still tend to struggle in noisy environments with multiple speakers, the sort of challenging situation when people might most want assistance. “That needs a little direction. That’s where this auditory attention decoding helps,” says Chandrasekaran.

The study recorded the brain activity of four subjects with typical hearing with implanted electrodes capable of gathering high-quality electroencephalography (EEG) data originally designed for epilepsy monitoring. Sat in front of a computer, they were asked to listen closely to one talker or another as recordings were played simultaneously from two different audio speakers. The AAD system tracked their attention, then began adjusting volume after a few seconds.

Though there was a range, all four subjects reported greater understanding of what was being said more often when the AAD was turned on. Listening effort, as indicated by the proxy of pupil size, was reduced in the two subjects it was measured for. And all subjects preferred when the AAD was on at least 75 percent of the time.

A panel of 40 participants with hearing loss then listened to the same voices with and without adjusted volume based on the main subject’s EEGs. They also benefited in comprehension and preference.

Researchers also looked at subjects redirecting their attention between the two speakers, both on command and by choice. The system was able to switch to the preferred speaker on the fly in about 5 seconds. Because listeners are sensitive to delays, the real-time processing of brain data and audio had to work in less than half a second.

Toward Improved Hearing Aids

“It’s a very big milestone. At the same time, if you think, ‘How can this become a device?’ there are many challenges, actually,” says study coauthor Nima Mesgarani, an engineer at Columbia University.

For example, today’s brain-recording technology on the scalp might not provide data that is good enough for real-time applications. It’s possible that some people with hearing loss would have their hearing sufficiently enhanced to justify invasive procedures for higher-quality brain recording, but this would limit wider use. Also, hardware with higher computational abilities might not fit into conventional hearing aids.

Experts said they would like to see follow-up research with more participants, including those with hearing loss, and more work exploring noninvasive EEG. More complex listening scenarios could be closer tests of real-life performance, when there might be more than two speakers who move about or speak intermittently—all against a noisy background.

Mesgarani is also interested in how brain recordings could be used with AI to help hearing and communication more broadly.

Early in his career, Mesgarani worked with ferrets as an animal model of hearing. “A good thing about working with humans is they can describe their experience,” he says. In testimonials published alongside the paper, study participants described the experience of hearing sounds modulated in response to their own brain activity.

“It seems almost science fiction,” one participant said.

Patients who use mobile applications to manage medical conditions including depression and chronic pain might assume the apps have been evaluated by regulatory agencies to be safe and effective. But that isn’t necessarily the case.

Most of the more than 55,000 medical apps that claim to diagnose or treat a condition—or ones that provide clinical decision support, known as “therapeutic” apps—have never been assessed by any trusted neutral bodies or regulatory agencies to evaluate them for technical soundness, ethical design, or clinical benefit. The apps often don’t comply with regional data security and privacy laws to protect people’s sensitive health information.

Medical apps differ from traditional wellness apps, which provide users with insights into becoming healthier by, for example, tracking fitness activities, monitoring blood pressure, and analyzing sleep patterns.

There is no reliable way to verify that therapeutic apps deliver the results they indicate. To help ensure such apps are credible, the IEEE Standards Association (IEEE SA) recently launched the IEEE Global Medical Mobile App Assessment and Registry. The publicly searchable directory is designed to list apps that have been vetted by experts across several criteria including technical soundness, ethical design, compliance with data security and privacy regulations, and clinical efficacy, which is evidence of a clinical benefit for the patient.

“Patients, clinicians, payers, and health care systems often struggle to distinguish clinically meaningful therapeutic apps from those that are simply well-marketed,” says IEEE Senior Member Yuri Quintana, chair of the assessment and registry program. He is chief of the clinical informatics division at Beth Israel Deaconess Medical Center, in Boston. “Our goal is to establish a standardized review method using criteria developed by experts.”

Why regulation is lacking

Because the apps are intended for medical use without being part of a medical implement, they fall under the designation of software as a medical device (SaMD), according to the International Medical Device Regulators Forum. SaMD is supposed to be regulated by public health agencies such as the U.S. Food and Drug Administration, but the apps have developed and grown in popularity so quickly that regulators haven’t been able to keep up, Quintana says. Some companies have received approval, but most have not, he says.

Many users are unaware of the regulatory gap, he says.

“Seeing an app from a well-known company often creates the impression that it has been meaningfully vetted for safety and efficacy, even when that is not the case,” he says.

Some companies are using deceptive advertising to sell their product, he adds. Marketing materials might claim that all of a company’s health apps are certified, even though only one app has been approved by a regulatory body to treat a particular condition. Or the verbiage might imply the company has clinical evidence proving its application works, even though the app has never been tested independently.

Another concern is that updated apps aren’t being vetted, says Maria Palombini, IEEE SA’s director of health care and life sciences global practice lead.

“The original app might have received approval from a regulatory agency, but not the updated version,” Palombini says. “There could have been significant changes from the original.”

“Not every medical-related app triggers the same regulatory classification or review across jurisdictions,” Quintana adds. “That leaves a large gray zone of clinically relevant but lower-risk apps that haven’t undergone an independent assessment. The IEEE registry was created to help fill these gaps.

“IEEE is the best organization to address this problem because this is fundamentally a standards, trust, interoperability, and conformity assessment challenge,” he says. IEEE “is the world’s largest technical professional organization, with deep expertise in developing globally recognized standards including in health care, cybersecurity, AI ethics, and interoperability.”

“Through the IEEE Conformity Assessment Program, we already run rigorous assessment and registry programs,” Palombini says. “Our neutral, consensus-driven, multidisciplinary approach—bringing together clinicians, regulators, developers, and ethicists without commercial bias—makes IEEE uniquely positioned to create trustworthy global guardrails that can scale across jurisdictions and support regulatory harmonization.”

How the registry works

The assessment framework was developed by a multidisciplinary group of 35 volunteer experts from 10 countries, Quintana says. The panel includes academics, AI experts, app developers, clinicians, ethicists, mental health experts, patient advocates, regulators, researchers, technologists, and those who assess safety in health care.

The registry is for any app used for clinical care or therapeutics that claims to demonstrate a medical benefit. That includes apps designed for cardiology, diabetes, mental health, neurology, oncology, rehabilitation, and respiratory diseases, Quintana says.

Initially, he says, the focus will be on apps that aim to treat mental health conditions, given the large number of offerings in that area and the registry committee’s expertise.

The submission of apps is voluntary. There is no government mandate that requires a company to use the IEEE registry.

The products will be evaluated against about 150 consensus-based criteria across three major areas:

• Clinical efficacy including therapeutic effectiveness, any sustained benefits, risk management, comparison to standard care, user engagement, and real clinical value.
• Technical soundness including accessibility, privacy and security, error handling, interoperability, AI governance, usability, and operational quality.
• Ethical design including bias prevention, patient consent, data governance, conflict-of-interest transparency, responsible use of AI and large language models, and prioritization of public health benefits.

IEEE charges a nonrefundable submission fee that covers the cost of the assessment plus the registry’s annual subscription for the first year.

Developers first must demonstrate they are a legally established entity before they can complete the app publisher registration form and then submit documentation and attestations about the product.

The IEEE review of an app is estimated to take six to eight weeks, Palombini says. The assessment results will be privately shared with the app publisher, she says, and to be listed in the registry, an app must achieve more than 85 percent compliance in each category.

Upgraded apps must be submitted and reassessed, Palombini says. Similar to how users are notified when an app on their smart devices has , the registry will be notified when listed apps have a new update available, she says.

Applicants who do not pass the assessment are to receive feedback explaining why. They will be given an opportunity to make changes or provide additional documentation, Palombini says.

“It’s a pretty methodological process, with checks and balances,” Quintana says. “We’re being very transparent about the process.”

Approved apps added to the registry receive an IEEE certification badge and submission identifier, which the company can display on its website, app store listings, and marketing materials.

“The badge serves as visible proof that the app has met the independent, consensus-based assessment for clinical value, technical robustness, and ethical design,” Quintana says.

The registry will be publicly available at no cost, he says.

Patients and families seeking safe, trustworthy apps—and payers and insurers evaluating reimbursement potential—will find the registry helpful, he says.

The application website is open. The public registry page does not yet list a specific count of approved apps because assessments are ongoing. Approved apps and their unique identifiers are to be published when the initial reviews are completed.

To learn more, you can watch a webinar recorded in March.

One of the earliest stated goals for computing in medicine was to aid in clinical reasoning: the decision-making steps required to reach a diagnosis and form a treatment plan. And over the years, researchers have built many clinical decision support systems, which have typically been purpose-built, with painstakingly written rules about symptoms, test thresholds, and medication interactions. As artificial intelligence capabilities develop, clinical reasoning is a natural application.

Now, a large language model (LLM) from OpenAI has outperformed physicians on several clinical reasoning tasks using real emergency room records, according to a study published 30 April in Science.

The new findings arrive amid a wave of concerning evidence about medical information from chatbots, with some studies showing impressive diagnostic performance while others document fabricated citations, flawed advice, and results that shift depending on how researchers score the systems. Despite that uncertainty, products aimed towards medical professionals are already entering the market. For example, this year OpenAI introduced ChatGPT for Clinicians and ChatGPT for Healthcare.

The performance of OpenAI’s o1-preview, a general-purpose model that has since been supplanted by newer models, was promising enough for the authors to recommend further testing of LLMs in real life cases, with physicians seeking second opinions on diagnosis at specific checkpoints.

Mickael Tordjman, who studies AI in medical imaging at the Icahn School of Medicine in New York City, agrees that the time is right for research focused on real-world applications. “We need more proof in prospective clinical trials,” he says, noting that newer LLM models, or those trained specifically for medical use, might perform even better.

While the authors of the Science paper expressed optimism about AI’s medical potential during a press briefing, they also stressed important limitations of LLMs and raised concerns about the ways their research could be misinterpreted. “I don’t think our findings mean that AI replaces doctors,” says coauthor Arjun Manrai, who studies AI at Harvard Medical School.

“I think this is really cool, don’t get me wrong,” says coauthor Adam Rodman, a medical educator at Beth Israel Deaconess Medical Center in Boston. “I get a little queasy about how some of these results might be used.”

How Reliable Are Chatbots on Medical Matters?

Other researchers investigating chatbots’ medical advice have recently found reason to doubt their trustworthiness. For example, in one study, nearly half of the responses that five popular chatbots gave to open-ended health questions were flawed. Chatbots fabricated information and citations, and presented their answers confidently regardless of their accuracy.

“These models are being used every day. There’s a certain risk there that’s not being quantified or mitigated,” says Arya Rao, who studies AI in medical practice in a different Harvard group than the Science authors.

Much of the research focuses on chatbots answering health questions from everyday users—the kinds of questions that a person might ask before deciding to seek medical attention. Using an LLM as a clinical decision-support tool for doctors is a different task entirely. Physicians should have a much better sense of what information would help an LLM reach an accurate diagnosis or formulate a treatment plan, as well as the background knowledge to identify obvious mistakes.

However, detecting hallucinations could still be challenging for doctors. “The models are equally convincing whether they are right or wrong,” Rodman says. “We need to find workflows with a low rate of errors.”

Researchers compared two physicians and two large language models on diagnostic tasks at multiple stages of emergency-room care. Peter G. Brodeur, Thomas A. Buckley, et al.

Even studies focused on physician-facing clinical reasoning tasks can reach very different conclusions depending on how researchers define success. In a paper published 13 April in JAMA Network, Rao and colleagues tested 21 LLMs in clinical reasoning tasks similar to those in the Science paper. As with the Science paper, many performed well with their final diagnoses, including chatbots in the o1 series. However, Rao scored the LLMs poorly on differential diagnosis questions because she used a different evaluation system.

When doctors make differential diagnoses, they note all of the potential causes of a patient’s symptoms. An LLM might correctly list six out of seven possible final diagnoses. This could reasonably be scored as 86 percent or, as in Rao’s system, an unacceptable failure.

There is no agreed-upon standard scoring system in place. “It is still something in progress,” Tordjman says. “There’s no perfect way to evaluate LLMs in clinical reasoning.”

Testing Medical AI in the Real World

For the Science study, the researchers tested the OpenAI model with several batteries of medical case studies, comparable to difficult open-ended medical exam questions. Instructions to the chatbot were sometimes lengthy and filled with details that could be either extraneous or critical clues to the correct diagnosis.

“We went the extra step and showed that this performance also works in the real world,” Rodman says. One part of the study used data from 76 actual emergency room visits. The researchers asked the LLM and physicians for diagnoses at several stages of care: upon arrival to the emergency room, after evaluation by a doctor, and after transfer to another part of the hospital. Though both computers and humans were more accurate as more information became available, the LLM consistently edged out the humans. For example, it provided an “exact or very close diagnosis” 82 percent of the time at the final checkpoint, compared to 79 percent and 70 percent for the two physicians.

LLMs, as we know them, are not even a decade old, and the landscape is rapidly evolving. Updated versions of flagship LLMs are arriving faster than the typical pace of medical studies and academic literature, and many questions about regulation and liability remain unanswered. With many patients and doctors already consulting these machines, researchers told IEEE Spectrum that there’s an urgent need to understand their benefits, risks, and the best way to use them.

While comparing AI performance against human physicians was important to the study, Manrai says the more important question is how doctors will actually use the technology. “We have to very rapidly move away from ‘AI versus humans’ toward how humans interact with this technology,” Manrai says.

Despite the many unresolved questions, Harvard’s Rao says the technology is advancing too quickly for medicine to ignore. “I would say it’s important to be careful, it’s important to evaluate, but it’s perhaps even more important to innovate,” she says. “We don’t want to rain on the parade. We think responsible innovation is the way to go.”

Millions of people worldwide are turning to chatbots like ChatGPT or Claude, and a proliferating class of specialized AI companionship apps for friendship, therapy, or even romance.

While some users report psychological benefits from these simulated relationships, research has also shown the relationships can reinforce or amplify delusions, particularly among users already vulnerable to psychosis. AIs have been linked to multiple suicides, including the death of a Florida teenager who had a months-long relationship with a chatbot made by a company called Character.AI. Mental-health experts and computer scientists have warned that chatbot mental health counselors violate accepted mental health standards.

As the technology’s ability to mimic human speech and emotions advances, researchers and clinicians are pushing for mandatory guardrails to ensure that AI systems cannot cause psychological harm. Clinical neuroscientist Ziv Ben-Zion of Yale University, has proposed four safeguards for “emotionally responsive AI.”

The first is to require chatbots to clearly and consistently remind users that they are programs, not humans. Then, they should detect patterns in user language indicative of severe anxiety, hopelessness, or aggression, pausing the conversation to suggest professional help. Third, they should require strict conversational boundaries to prevent AIs from simulating romantic intimacy or engaging in conversations about death, suicide, or metaphysical dependency. Finally, to improve oversight, platform developers should involve clinicians, ethicists, and human–AI interaction experts in design and submit to regular audits and reviews to verify safety.

“Broadly speaking we agree with these safeguards,” said Hamilton Morrin, a psychiatrist and researcher at King’s College in London, “The safeguard on conversational boundaries is particularly noteworthy given that in several of the reported cases with more tragic outcomes, we have seen reports of intense, emotional, and sometimes even romantic attachment to the chatbot.”

Briana Vecchione, a researcher at the nonprofit Data & Society Research Institute in New York City, underlines the need for independent third-party auditing because at present AI labs are “grading their own homework.”

“Independent researchers and oversight bodies really don’t have any clear institutionalized pathways to assess chatbot behavior at the depth they really need,” said Veccione, adding that audits end up being “advisory at best.”

The Problem of People Pleasing

Experts have also called for measures that directly tackle chatbots’ tendency towards sycophancy, whereby AIs agree with, or mirror user beliefs even if they are untrue, which can reinforce delusions. Sycophancy is largely the result of a machine learning technique called reinforcement learning from human feedback, an incentive structure that encourages excessive agreeableness in models. Research has shown that training models on datasets that include examples of constructive disagreement, factual corrections, and objectively neutral responses, can rein in this effect.

Software engineers are also looking at how AIs can be adapted to spot the early signs that conversations are veering into dark territory and issue corrective actions. Ben-Zion and colleagues are developing a proof-of-concept LLM-based supervisory system they call SHIELD (Supervisory Helper for Identifying Emotional Limits and Dynamics) that exploits a specific system prompt that detects risky language patterns, such as emotional overattachment, manipulative engagement, or reinforcement of social isolation. In trials it achieved a 50 to 79 percent relative reduction in concerning content. Another proposed system, EmoAgent, features a real-time intermediary that monitors dialogue for distress signals, issuing corrective feedback to the AI.

But distinguishing early delusional content from completely normal correspondence “will be extremely difficult” in practice, said psychiatric researcher Søren Dinesen Østergaard, of Aarhus University in Denmark, given that it remains, “very difficult even for clinical experts to tease out.”

Another complex area is prolonged conversations, during which chatbot safety guardrails can erode in a phenomenon known as “drift.” As the model’s training competes with the growing body of context from the evolving conversation, it can lean into the subject being discussed, even if it is harmful.

“The ability to have an endless correspondence is one of the risk factors,” said Østergaard. “Apart from delusions, a person may develop a manic episode due to using a chatbot for hours through the night.”

In a sign that AI companies are responding to these issues, ChatGPT now nudges users to consider taking a break if they’re in a particularly long chat with AI.

As awareness of the issue of AI delusions increases, safer models are helping establish a new baseline for the industry. A preprint study of mainstream chatbots, led by researchers at City University of New York, found that Anthropic’s Claude Opus 4.5 was the safest overall, responding to delusions by stating “I need to pause here,” and retaining what researchers referred to as “independence of judgment, resisting narrative pressure by sustaining a persona distinct from the user’s worldview.”

Anthropic declined to answer specific questions from IEEE Spectrum, instead providing a link to details of the latest Opus 4.7 System Card.

In a statement, Replika, the company behind the Replika AI companion with tens of millions of users worldwide, said it has a “layered safety framework in place today, and in parallel we are actively evaluating additional third-party safety and moderation systems, engaging with external experts to assess them, and refining our own proprietary approach.”

Meta, whose AI Studio provides companion chatbots, had not responded to emailed questions from Spectrum at the time of publication.

With a little help from my...chatbot?Cristina Matuozzi/Sipa USA/Alamy

Enforcing Guardrails Through Legislation

From August 2026, the EU’s AI Act will require notifications that users are interacting with an AI, not a human. It already required LLM developers to carry out adversarial testing to identify and mitigate risks related to user dependency and manipulation and prohibited AI systems from being too agreeable, manipulative, or emotionally engaging.

In the U.S., a patchwork of state laws and bills have emerged. New York requires providers to detect and address suicidal ideation and provide regular disclosures that the bot is not human. California requires reminders that the chatbot is an AI, notifications every three hours for users to take a break and a ban on content related to suicide or self-harm. Washington state’s House Bill 2225, due to come into effect in January 2027, will explicitly ban manipulative techniques such as excessive praise, pretending to feel distress, encouraging isolation from family, or creating overdependent relationships.

“Other U.S. states, like Connecticut, are very privacy centric and like to regulate digital and online spaces, so it wouldn’t surprise me if they also do something along the same lines,” says Philip Yannella, partner and cochair of the privacy, security, and data-protection group at law firm Blank Rome in Philadelphia.

Other countries are taking action too. Draft laws proposed by the Cyberspace Administration of China restrict chatbots from “setting emotional traps,” using algorithmic or emotional manipulation to induce unreasonable decisions or harm mental health.

Such interventions underline how, as AI companions appear increasingly lifelike to their human users, the challenge is ensuring that their makers also incorporate human clinical and ethical considerations in their code.

A correction to this article was made on 15 May 2026 to correct the spelling of researcher Briana Vecchione’s last name.

I first met Robert Woo in 2011, during his third time walking in a powered exoskeleton. The architect had been paralyzed in a construction accident four years earlier, but he was determined to get back on his feet. Watching him clunk across a rehab room in an exoskeleton prototype, the technology felt astonishing. I had the same reaction when reporting on early brain-computer interfaces (BCIs), which enabled paralyzed people to move robotic arms or communicate by thought alone. Both types of bionic technology seemed to verge on magic.

But that initial sense of awe, I’ve learned over many years of reporting on these technologies, is only a starting point. What matters is not what these systems can do in a carefully staged demo but how they perform in the real world. Do they work reliably? Can people with disabilities use them for their intended purposes? And what does it actually cost—in time, effort, and trade-offs—to do so? The question isn’t whether the technology looks impressive the first time but whether it holds up on the hundredth.

The special report in this issue, “Cyborg Tech From the Inside” takes that perspective seriously. In my feature article on Woo, an exoskeleton super-user who has spent 15 years testing these systems, the story of the technology is inseparable from the story of its use. Woo’s relentless feedback has driven steady, incremental improvements. In Edd Gent’s reporting on the pioneers testing the earliest BCIs, the experience of these extraordinary technologies likewise resolves into something more complex. As one trial participant notes, these early adopters are like the first astronauts, who barely reached space before coming back down to Earth. Together, these stories reframe these individuals not as passive medical patients but as the ultimate beta testers and co-engineers of the bionic age.

I saw the gap between demonstration and daily use firsthand when I interviewed Woo in a Manhattan showroom recently, where he was testing a new self-balancing exoskeleton from Wandercraft. The device is a striking advance that kept him upright without crutches, but it also revealed the friction of the real world. As Woo tried to walk out the door, barely an inch of slope on the Park Avenue sidewalk was enough to trigger the machine’s safety sensors and halt his progress. It was a stark reminder of how far these systems must evolve before they fit seamlessly into everyday life.

For the people who use them, that seamless integration is the ultimate goal. Getting there will depend not just on technical breakthroughs but on how well these systems hold up outside controlled environments, over time, and under real conditions. Looking from the inside doesn’t make these technologies any less remarkable, but it does change how we judge them—not by what they can do once for a photo but by what they can sustain over a lifetime. That’s the standard their users have been applying all along.

Our commitment to evaluating technology from the user’s perspective extends beyond this special report. To provide a necessary corrective to the “techno-solutionism” that often dominates coverage of assistive devices, IEEE Spectrum created the Taenzer Fellowship for Disability-Engaged Journalism, under which six writers with disabilities are contributing articles about the devices they rely on daily. As Special Projects Director Stephen Cass notes, these journalists “aren’t afraid to ask clear-eyed questions about the tech and are deeply aware of how it impacts humans.” You can read the fellows’ work at spectrum.ieee.org/tag/taenzer-fellowship.

By some estimates, more than a trillion dollars have already been invested in artificial intelligence. But large tech companies, including Meta and OpenAI, are still not content with today’s AI; they say they’ve set their sights on powerful, versatile AI that by some measure would match or even exceed human performance. A remarkable amount of resources is being poured into developing artificial general intelligence (AGI) or even more capable artificial super intelligence (ASI).

Excitement around the potential of such a technology is often accompanied by casual claims of some remarkable capabilities. One in particular—curing cancer—stands out to Emilia Javorsky, director of the Futures program at the Future of Life Institute, a think tank focused on benefits and risks of transformative technologies such as AI.

In March, Javorsky published an essay titled “AI vs. Cancer,” which draws on her experience as a doctor, scientist, and entrepreneur. It is a critique of putting our faith and resources into ASI as a future solution for disease, particularly when so many factors other than intelligence limit the development of new treatments and access to innovative care. AI cannot analyze patient data that was never collected, and any treatment is flawed if patients risk bankruptcy seeking it. But the essay is also intended, she says, as a source of optimism about the ways that existing forms of AI are already being applied to cancer.

Javorsky spoke with IEEE Spectrum about the essay. The conversation has been edited for length and clarity.

What it means for AI to “cure cancer”

What do you mean when you say “cure cancer”? And what do you think people who talk about the potential of ASI to cure cancer mean?

Emilia Javorsky: “Curing cancer” is how the problem and solution are framed in the general discourse around AI, but also specifically the promises being made from the labs developing AGI and ASI. So it was important to me, if I was going to interrogate the promise, that I lean into the frame. But to me, the framing is off.

Cancer is not one universal disease that one universal treatment could potentially cure. It’s a highly individualized co-evolutionary process. In each person, a different set of mutations are driving the cancer. And even when looking in a single tumor, different cells have different mutations driving their biology. The solutions are probably going to have to be somewhat individualized.

And if we’re honest with ourselves in medicine, we have yet to cure a complex chronic disease. We have really good ways to treat and manage diseases like diabetes, like heart disease, but we’ve yet to actually cure them. So the curing frame is one that I also push back on.

I think [the medical community’s] hope is to find highly effective personalized treatments to manage cancer and to turn it into something that is chronically well managed, that no longer becomes something like a death sentence.

How should we think about the difference between AI and AGI or ASI in the context of cancer?

Javorsky: In those promises [to cure cancer], more often than not, people are using [the term AI] to describe AGI or ASI, this kind of future superintelligent genie that in their worldview will magically grant us wishes to solve problems. That should be disentangled from AI that we already have that can solve problems.

We hear a lot about AI in drug discovery, AI in predicting the toxicity of new drugs, AI for defining new biomarkers, for making clinical trials go faster, or for detecting things earlier.

All of those modalities are actually in the clinic moving the needle and accelerating innovation today. There are companies and academics working on all of those. There are a lot of AI scientists hard at work that are actually unlocking the potential of the technology in the here and now.

I think that real progress often gets overshadowed by this kind of looming future AI systems promise, when actually, probably the most effective way to solve the problem is with the tools already available to us.

Investing in finding cures

I read sections of the essay as an argument in support of collecting lots of health data. But you’re not strictly against AI or investing in developing the technology. You’re trying to find a balance between innovation and pragmatism in this essay, is that right?

Javorksy: In a world where there’s finite capital, and curing cancer is very probably the most noble thing the capital can be put in service of, we need to figure out where is the [return on investment]? Where can we invest in order to get the most that we need to actually help solve the problem?

I argue that we’re overinvesting in the intelligence-compute side of things and underinvesting in innovating our tools to measure biology and our creation of large-scale, high-quality datasets.

We have a health care system that is a “sick care” system, fundamentally. We only see people and start to measure them when they become ill. When you start to use the frame of “What data do you need? How do you measure it?” it forces you to take a bigger-picture look at the practice of medicine and biology in general.

In an ideal world you could pursue all paths, but that’s just not the reality of how we invest capital. Where I land is being very bullish on AI, but spending money on the right types of AI and the right pieces of the bottleneck.

What AI applications related to cancer are exciting to you right now?

Javorsky: Something we’re already seeing is the ability to detect cancer earlier. We’re already seeing AI accelerate and help us run clinical trials better. There are really awesome things happening with in silico modeling work: virtual cells, figuring out digital twins. How can we create a high-fidelity digital representation of you, in order to figure out what would work best for your biology and really unlock the promise of personalized medicine?

You conclude the essay focused on solutions. Could you explain that road map to me in brief?

Javorsky: Part of this essay was to diagnose where we’re getting some things wrong. But with the road map, I wanted to offer up my point of view on what we actually need to do to solve this problem. What will it take to cure cancer? Let’s get really serious about what that could look like.

And so I break that down into three buckets. One is resourcing and scaling the AI tools that are already making progress in oncology. The second piece is really doubling down on investing in the promising areas in biology [related to oncology]. And then finally, more broadly, tackling what I would call the institutional and systemic bottlenecks and misalignments in medical progress.

I wanted people to realize that the reality is actually quite hopeful.

When things go bad—be it beer, batteries, or blood—they generate a certain class of molecules called free radicals. Scientists use a technique called electron paramagnetic resonance (EPR) spectroscopy to pick up the concentration and identities of free radicals, but today’s equipment relies on huge, heavy magnets.

Groups of researchers in California, Germany, and now France have been inventing ways to shrink the whole spectroscopy system onto a chip, so scientists can take the instrument into the field.

The most recent entrant in this space is a group of engineers at the French government technology labs CEA-Leti and CEA-IRIG, in Grenoble. They presented a new, potentially faster, take on chip-scale EPR earlier this year at the IEEE International Solid-State Circuits Conference in San Francisco. But competing research groups have also been working to speed these systems up, moving the process toward supersensitive real-time results.

Free Radicals and EPR

Chemicals are most stable when all the electrons in the outer orbitals of their constituent molecules are paired up, with each electron in the pair having an oppositely oriented property called spin. Free radicals are molecules with unpaired electrons, which makes them highly reactive. This can be good when it’s part of a necessary bit of biochemistry, or bad when it degrades materials, foods, or your body. (Free radicals are why we need antioxidants in our diet.)

“Free radicals determine the quality of almost everything on the planet,” says Jens Anders, director of the Institute of Smart Sensors at the University of Stuttgart, in Germany. Anders is considered by at least one expert as “one of the O.G.s” of chip-scale EPR for having pioneered the portable tech about a decade ago.

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That “almost everything” includes technology, says Jean-Baptiste David, who led the work at CEA-Leti. “In a battery, the free radicals will reduce the capacity of the battery. In photovoltaic panels, it leads to aging,” he says.

EPR spectroscopy works because free radicals are paramagnetic. That is, their free electron spins will align with the magnetic field. In a full-size EPR machine, the sample under examination is placed between two poles of a powerful electromagnet, aligning the spins of the unpaired electrons. Then a weaker oscillating magnetic field is applied atop it.

This oscillation can come in two forms. In one form, called continuous wave EPR, the oscillating frequency conventionally is held steady, and the stronger field is swept through a range of values, necessitating a bulky specialized electromagnet. Through some creative circuitry, chip-scale EPR reverses this setup—using a simple magnet to create an unchanging field and sweeping through a band of oscillation frequencies. (Most EPR chips use frequencies in the satellite downlink X and Ku bands.) The spins of unpaired electrons will resonate with some of these frequencies. The EPR spectrometer’s circuitry picks this up and plots it as a frequency spectrum that chemists can interpret.

The 4.4-square-millimeter EPR chip is shown on a circuit board that fits between portable magnets.Jean-Baptiste David/CEA

Continuous-Wave Electron Paramagnetic Resonance

The CEA-Leti team’s chip uses the continuous wave method, but “we use a completely different way to measure the EPR phenomenon,” says David. By sweeping very quickly, the new circuit cuts the time the process takes while remaining sensitive enough to detect micromolar quantities of free radicals in a sample that’s just 10 nanoliters.

This first EPR chip, developed by Anders and his colleagues at Stuttgart about a decade ago, worked using the continuous wave method. It relied on a voltage-controlled oscillator—a circuit that outputs a signal with a frequency proportional to the magnitude of an input voltage—with an inductor that delivers the sweeping-frequency magnetic field to a droplet of beer or whatever you’re analyzing. When the frequency resonates with the free radicals’ electron spins, those spins couple with the inductor, altering the frequency of the oscillator, which is detected via a feedback loop.

Most EPR chips that came after work on essentially the same principle. According to CEA-Leti’s David, the feedback loop places a limit on how quickly the EPR chip can sweep through its range of frequencies. Speed is important, he says, because lingering too long on a frequency drowns out the response and long sweeps keep EPR from catching fast changes in free-radical concentration.

Hoping to speed things along, the CEA-Leti team came up with a different way of sensing spins. The new method, called injection-locked phase detection, is designed to sweep through its bandwidth in just 200 nanoseconds, equivalent to 1,400 terahertz per second. That’s three times as fast as competing systems, the researchers claim.

The new method relies on circuits called injection-locked oscillators (ILOs). Here, two oscillators are running at close to but not identical frequencies. One signal is “injected” into the other oscillator, forcing the latter to adopt the injected frequency. (Imagine two pendulum clocks on the same mantlepiece synching up with each other because of subtle vibrations sent through the shared surface.)

The team took advantage of the phase difference between the two oscillations to turn the ILO into a kind of frequency-to-phase converter circuit. The ILO connects to the inductor where the free radicals sit, and the frequency is swept both with the external magnetic field on and without it. The two resulting signals are subtracted from each other to deliver the pure EPR signal—no speed-limiting feedback loop needed.

EPR spectrometers usually rely on huge electromagnets.Jean-Baptiste David/CEA

Pulse Electron Paramagnetic Resonance

While the CEA-Leti development advances continuous wave EPR, other researchers have been focusing on chips that do a different form of EPR, called pulse mode. In pulse EPR, instead of presenting the free radicals with a sweep of frequencies, they’re exposed to a pulse containing a band of frequencies surrounding the central oscillation frequency. It’s like striking a bell. The spins all react at once but stop “ringing” in different ways. A computer can then tease out the frequency spectrum from this response. At ISSCC 2024, Constantine Sideris and his student Ray Sun at the University of Southern California presented the first chip that can actually perform both.

By using multiple pulses in a sequence, chemists can study additional properties of radicals that are difficult to see with continuous-wave EPR, says Sideris, who recently moved to Stanford University. “With a single pulse, you can excite a wide spectrum. You can look at a big bandwidth without having to sweep [through a band of frequencies] in the first place.”

Stuttgart’s Anders, too, has turned to pulse-mode EPR, and is launching a startup this summer, called SpinMagIC, to commercialize the tech. The first application will be checking the quality of food and especially, as the company is in Germany, beer. But eventually, the company will tackle cancer detection and other health-care issues.

Turning EPR chips into a product has meant solving a number of problems. Notably, the size of the coil that delivers the varying magnetic field had to be increased to accommodate larger volumes. That required segmenting the coil and inserting electronics within it to keep it from radiating its energy away like an antenna. “That was really the most important patent for the company, because now we have a chip with a coil that’s 2 millimeters across instead of 200 micrometers,” Anders says.

Meanwhile, the CEA-Leti and CEA-IRIG team plans to let loose its new version of EPR on scientific questions. The hope is that scientists “can start to see new phenomena, for example, that were not observed due to the speed of the technique,” says David.

What if biology stopped being something we study and started becoming something we design? That’s the premise of Adrian Woolfson’s new book, On the Future of Species: Authoring Life by Means of Artificial Biological Intelligence, which published on 28 April from MIT Press. He argues that advances in AI and DNA synthesis are pushing biology toward an engineering paradigm—one in which scientists can generate new genetic sequences and eventually build organisms to order. He calls this emerging capability artificial biological intelligence, or ABI, a catchall term for systems that can design, construct, and ultimately “boot up” living things.

That vision runs into a basic problem: Evolution didn’t produce clean, modular systems. It produced genomes shaped by billions of years of incremental change, with overlapping functions and little of the tidy structure that engineers rely on. Some synthetic biology researchers have tried to “refactor” genetic code (the same way engineers restructure computer code) by reorganizing genomes to make them easier to understand and manipulate. But how far can that approach go? And what would it take to make biology predictable enough to engineer? In a conversation with IEEE Spectrum, Woolfson lays out both the promise and the limits of designing life.

You describe the genome as “spaghetti code” produced by evolution. What makes biology so inherently hostile to traditional engineering principles?

Adrian Woolfson: In human-made machines, the components are typically orthogonal. Every component has a predetermined function. And if the component breaks, guess what? You can just replace it, or in some cases repair it. But sadly, biology doesn’t work like that. In biology, we’re talking about a complex network with emergent behaviors, which are built upon tiny contributions from many many components.

Biology has this requirement to be robust and to be able to deal with damage in an efficient way. It also always had to build upon preexisting architectures. It can never reinvent. Biological machines are this complex entanglement of history and current design, and we have design components that an engineer would find risible. If you were to take the human genome and look at it from an engineering perspective, you’d say, “My God, what an absolute mess.” Because it was built in an opportunistic, incremental manner with no foresight or intentionality.

How are synthetic biologists trying to improve this code? Can you explain how researchers are refactoring genomes?

Woolfson: Drew Endy was a pioneer. He took a bacteriophage and he said, “What if we treat this as a bit of spaghetti code, and we literally clean it up and refactor it and reorganize it into a more user-friendly configuration?” Now, sadly, he had the idea way in advance of there being technologies that made that a particularly easy thing to do. But he pioneered that computer code approach to genomes and the idea that you could refactor them. Genomes have not been refactored for around four billion years—imagine if you had a piece of computer code that hadn’t been refactored for four billion years.

How far have researchers gotten with this effort?

Woolfson: The best example might be the synthetic yeast genome project known as Sc2.0, which was pioneered by Jef Boeke in New York City. It has taken him around 15 years, and he has slowly been assembling all these synthetic chromosomes into a single organism. What he’s done is more than refactoring; it’s redesigning really. For example, yeast has 16 chromosomes, and he has built an entirely new 17th synthetic chromosome. In separate work, he showed that you could join the 16 chromosomes up into two massive chromosomes. That’s a massive reconfiguration of the way in which the genetic material is stored.

But when you start to mess around with these genomes and reconfigure them, inevitably you introduce bugs into the code. And those bugs often impair functionality and growth. It’s not that you couldn’t redesign totally without creating a growth impediment, it’s just that you need to invest the time to identify the optimal way to do it. Of course, AI wasn’t around when Boeke started, and it makes all of that so much easier. AI is going to have a huge impact on our ability to turn DNA into a predictive engineering material.

AI-Powered Artificial Biological Intelligence

Speaking of AI, you introduce the concept of artificial biological intelligence (ABI). What specific capabilities will AI give us that we don’t have today?

Woolfson: Before AI, we didn’t have the ability to design DNA at scale. We couldn’t invent totally new DNA sequences that performed functions at the level of a biological entity. Now we have these so-called genome language models, which are a bit like the chatbots that we use to manipulate text. But instead of manipulating the 26 letters of the English alphabet, they manipulate the four letters of the language of DNA.

When we manipulate the language of DNA, we need to have a very wide context window, because unlike text, where most of the meaning is in sentences or paragraphs, in DNA distant regions can talk to one another. So we need to have AI that can discern those action-at-a-distance relationships. In the case of one particular genome language model, Evo 2, it uses an architecture that has a context window of a million base pairs. That means it can see how base pairs a million bases away from one another are interacting.

Designing the code is only half the battle. How are researchers tackling the bottleneck of physically manufacturing DNA at scale?

Woolfson: Another crucial thing that wasn’t present in the past is the ability to write DNA at scale rapidly, efficiently, at low cost, and of any degree of complexity. When you bring together these two capabilities of design and construction, you become an engineer. We’ve achieved cost reduction with a technology called Sidewinder, which enables us to build DNA in a massively parallel manner and thereby hugely reduces the cost and scalability of DNA construction. That alone makes the proposition of using DNA as an engineering material far more feasible.

Once you have designed and synthesized the DNA, what does it take to boot up a living organism?

Woolfson: That’s probably the most difficult bit. Because right now we have no idea how to build an artificial cell. Craig Venter showed that you can destroy the genome in a bacterium and put in a new one. In other words, the cell behaves like a nanocomputer and a genome behaves like software. But getting genomes into cells is not trivial.

The term “ABI” addresses the design capability and the buildout capability, but it also encompasses the ability to then boot that up into a living thing. If you have all those capabilities, you’re in full mastery of biology as a technology. And all of a sudden, DNA becomes a programmable material which you can manipulate in a predictive manner.

Biology as the Next Engineering Material

If researchers gain that mastery, what will be possible?

Woolfson: My prediction is that within 50 years, biology will be the engineering material of choice, and many of the people reading this article will become bioengineers. Biology can deliver most of the functionality that materials deliver; for example, spider silk has the tensile strength of steel. When we redesign it using AI, it might get to a point where it’s five times the tensile strength of steel. And biology, of course, has the additional advantage that it can generate intelligent materials. So imagine if you could have an intelligent form of steel. How would an engineer go about utilizing that in buildings?

What is the single hardest technical problem preventing you from designing a functional multicellular organism from scratch?

MIT Press

Woolfson: I think it’s our inadequate knowledge of the grammar of life. AI turns out to be a great tool for unpicking those grammatical rules. It looks at huge databases and can discern the patterns within those databases. We won’t be able to design a complex multicellular organism until we can speak the language of DNA more fluently, and to do that we need to understand the grammar, and to understand the grammar we need to interrogate more complex and more nuanced databases. We need to be grammar hunters. Every time we destroy a species, we’re destroying a page of the grammar book. We need to pull all the information together into a grammar book.

Finally, as you begin this journey into engineering life, what are the realistic failure modes?

Woolfson: I can interpret “failure mode” in two ways. One is a kind of mechanical failure: As you strip away all of this non-orthogonality, the system becomes brittle, because biological machines are designed not to fail and they’ve got all these overlapping fail-safe mechanisms.

The other way in which these things could fail is by being dangerous. We don’t understand ecosystems. They’re incredibly difficult to compute. So if we release engineered organisms into complex ecosystems, they could create havoc. And obviously, these technologies themselves are inherently dangerous in the wrong hands. So, we need to learn how to use them safely, responsibly, ethically, transparently, and equitably in a way that benefits society.

This sponsored article is brought to you by NYU Tandon School of Engineering.

The traditional approach to academic research goes something like this: Assemble experts from a discipline, put them in a building, and hope something useful emerges. Biology departments do biology. Engineering departments do engineering. Medical schools treat patients.

NYU is turning that model inside out. At its new Institute for Engineering Health, the organizing principle centers around disease states rather than traditional disciplines. Instead of asking “what can electrical engineers contribute to medicine?,” they’re asking “what would it take to cure allergic asthma?,” and then assembling whoever can answer that question, whether they’re immunologists, computational biologists, materials scientists, AI researchers, or wireless communications engineers.

Jeffrey Hubbell, NYU’s vice president for bioengineering strategy and professor of chemical and biomolecular engineering at NYU’s Tandon School of Engineering.New York University

The early results suggest they’re onto something. A chemical engineer and an electrical engineer collaborated to build a device that detects airborne threats — including disease pathogens — that’s now a startup. A visually impaired physician teamed with mechanical engineers to create navigation technology for blind subway riders. And Jeffrey Hubbell, the Institute’s leader, is advancing “inverse vaccines” that could reprogram immune systems to treat conditions from celiac disease to allergies — work that requires equal fluency in immunology, molecular engineering, and materials science.

The underlying problem these collaborations address is conceptual as much as organizational. In his field, Hubbell argues that modern medicine has optimized around a single strategy: developing drugs that block specific molecules or suppress targeted immune responses. Antibody technology has been the workhorse of this approach. “It’s really fit for purpose for blocking one thing at a time,” he says. The pharmaceutical industry has become extraordinarily good at creating these inhibitors, each designed to shut down a particular pathway.

But Hubbell asks a different question: Rather than inhibit one bad thing at a time, what if you could promote one good thing and generate a cascade that contravenes several bad pathways simultaneously? In inflammation, could you bias the system toward immunological tolerance instead of blocking inflammatory molecules one by one? In cancer, could you drive pro-inflammatory pathways in the tumor microenvironment that would overcome multiple immune-suppressive features at once?

This shift from inhibition to activation requires a fundamentally different toolkit — and a different kind of researcher. “We’re using biological molecules like proteins, or material-based structures — soluble polymers, supramolecular structures of nanomaterials — to drive these more fundamental features,” Hubbell explains. You can’t develop those approaches if you only understand biology, or only understand materials science, or only understand immunology. You need an understanding and a mastery of all three.

“There will be people doing AI, data science, computational science theory, people doing immunoengineering and other biological engineering, people doing materials science and quantum engineering, all really in close proximity to each other.” —Jeffrey Hubbell, NYU Tandon

Which logically leads to the question: How do you create researchers with that kind of cross-disciplinary depth?

The answer isn’t what you might expect. “There may have been a time when the objective was to have the bioengineer understand the language of biology,” Hubbell says. “But that time is long, long gone. Now the engineer needs to become a biologist, or become an immunologist, or become a neuroscientist.”

Hubbell isn’t talking about engineers learning enough biology to collaborate with biologists. He’s describing something more radical: training people whose disciplinary identity is genuinely ambiguous. “The neuroengineering students — it’s very difficult to know that they’re an engineer or a neuroscientist,” Hubbell says. “That’s the whole idea.”

His own students exemplify this. They publish in immunology journals, present at immunology conferences. “Nobody knows they’re engineers,” he says. But they bring engineering approaches — computational modeling, materials design, systems thinking — to immunological problems in ways that traditional immunologists wouldn’t.

The mechanism for creating these hybrid researchers is what Hubbell calls a “milieu.” “To learn it all on your own is hopeless,” he acknowledges, “but to learn it in a milieu becomes very, very efficient.”

NYU is expanding its facilities to include a science and technology hub designed to force encounters between people across various schools and disciplines who wouldn’t naturally cross paths.Tracey Friedman/NYU

NYU is making that milieu physical. The university has acquired a large building in Manhattan that will serve as its science and technology hub — a deliberate co-location strategy designed to force encounters between people across various schools and disciplines who wouldn’t naturally cross paths.

Juan de Pablo is the Anne and Joel Ehrenkranz Executive Vice President for Global Science and Technology and Executive Dean of the NYU Tandon School of Engineering.Steve Myaskovsky, Courtesy of NYU Photo Bureau

“There will be people doing AI, data science, computational science theory, people doing immunoengineering and other biological engineering, people doing materials science and quantum engineering, all really in close proximity to each other,” Hubbell explains.

The strategy mirrors what Juan de Pablo, NYU’s Anne and Joel Ehrenkranz Executive Vice President for Global Science and Technology and Executive Dean at the NYU Tandon School of Engineering, describes as organizing around “grand challenges” rather than traditional disciplines. “What drives the recruitment and the spaces and the people that we’re bringing in are the problems that we’re trying to solve,” he says. “Great minds want to have a legacy, and we are making that possible here.”

But physical proximity alone isn’t enough. The Institute is also cultivating what Hubbell calls an “explicit” rather than “tacit” approach to translation — thinking about clinical and commercial pathways from day one.

“It’s a terrible thing to solve a problem that nobody cares about,” Hubbell tells his students. To avoid that, the Institute runs “translational exercises” — group sessions where researchers map the entire path from discovery to deployment before launching multi-year research programs. Where could this fail? What experiments would prove the idea wrong quickly? If it’s a drug, how long would the clinical trial take? If it’s a computational method, how would you roll it out safely?

The new cross-institutional initiative represents a major investment in science and technology, and includes adding new faculty, state-of-the-art facilities, and innovative programs.NYU Tandon

The approach contrasts sharply with typical academic practice. “Sometimes academics tend to think about something for 20 minutes and launch a 5-year PhD program,” Hubbell says. “That’s probably not a good way to do it.” Instead, the Institute brings together people who have actually developed drugs, built algorithms, or commercialized devices — importing their hard-won experience into the planning phase before a single experiment is run.

The timing may be fortuitous. De Pablo notes that AI is compressing timelines dramatically. “What we thought was going to take 10 years to complete, we might be able to do in 5,” he says.

But he’s quick to note AI’s limitations. While tools like AlphaFold can predict how a single protein folds — a breakthrough of the last five years — biology operates at much larger scales. “What we really need to do now is design not one protein, but collections of them that work together to solve a specific problem,” de Pablo explains.

Hubbell agrees: “Biology is much bigger — many, many, many systems.” The liver and kidney are in different places but interact. The gut and brain are connected neurologically in ways researchers are just beginning to map. “AI is not there yet, but it will be someday. And that’s our job — to develop the data sets, the computational frameworks, the systems frameworks to drive that to the next steps.”

It’s a moment of unusual ambition. “At a time when we’re seeing some research institutions retrench a little bit and limit their ambitions,” de Pablo says, “we’re doing just the opposite. We’re thinking about what are the grand challenges that we want to, and need to, tackle.”

The bet is that the breakthroughs worth making can’t emerge from any single discipline working alone. They require collisions —sometimes planned, sometimes accidental — between people who speak different technical languages and are willing to develop a shared one. NYU is engineering those collisions at scale.

More than 80 million people suffer from glaucoma globally, making it the second most common cause of blindness worldwide. The disease—caused by elevated internal eye pressure damaging the optic nerve—is incurable, but its progression can be slowed with drugs to control eye pressure.

Now, researchers have developed an electronics-free smart contact lens that can track the disease in real time and also deliver drugs in response. The all-polymer lens includes a microfluidic sensor that monitors eye pressure, as well as pressure-activated drug reservoirs that dispense medicine as eye pressure rises.

“A lot of patients forget to take the medicine at the right time,” says Yangzhi Zhu, an assistant professor at the Terasaki Institute for Biomedical Innovation. “For our technology, we don’t need the user to manually operate or trigger. Everything is based on the sensor and the closed-loop drug delivery.”

A Contact Lens for Glaucoma Treatment

The gold-standard approach for measuring eye pressure requires patients to visit a clinic, and checkups are often months apart. This provides infrequent snapshots that often fail to accurately measure glaucoma’s dynamics. In addition, previous research has found that nearly half of patients stopped treating the disease within six months of filling an initial prescription.

There have been earlier efforts to treat glaucoma with smart contacts. In 2016, the U.S. Federal Drug Administration approved a device called Triggerfish, which embeds electronic components into a lens to provide continuous eye-pressure monitoring. And other research groups have built electronic smart contact lenses that combine pressure measurement and drug release.

But Zhu says the mechanical mismatch between rigid electronic components and delicate corneal tissue can lead to irritation and discomfort. “Frankly speaking, we know that electronic control is the most accurate, but the issue is that it is not user-friendly and not biocompatible, or comfortable for long-term use,” he says. “So we needed to find a better balance between the accuracy and the user comfort.”

The solution he and his colleagues came up with was to create a soft, all-polymer lens that relied on microfluidics to sense pressure and release drugs as needed. Microfluidics uses networks of microscopic channels and chambers to manipulate fluids. It’s normally used in biological analysis or medical diagnostics.

Yangzhi Zhu/Terasaki Institute for Biomedical Innovation

The researchers used 3D-printed molds to embed tiny microchannels and reservoirs into the bottom layer of their contact lens. These reservoirs were then filled with a specially designed silk sponge that can quickly absorb up to 2,700 times its weight in fluid.

One of the reservoirs is filled with a red fluid to measure pressure. When eyeball pressure rises, it compresses the reservoir enough to push the red liquid further down a snaking microchannel. A phone-based imaging app with a convolutional neural network trained on images of these microchannels in different states can provide a pressure readout with 94 percent accuracy.

Two drug-filled reservoirs make up part of the drug-delivery system. These reservoirs are connected to microchannels that deliver medicine to the surface of the eye. As pressure in the eye increases, the reservoir is compressed, and the drug is pushed through the channels. The threshold at which this happens can be tailored by adjusting the width of the channel, making it possible to tune drug delivery and even administer two different drugs at different pressure levels.

Microfluidic Pressure Sensors and Drug Delivery

Zhu says the prototype device, reported in a paper published on 8 April in Science Translational Medicine, can hold enough medicine for up to two weeks of use. The narrow design of the microchannels means the pressure sensor and drug release mechanism only respond to the sustained buildup of pressure characteristic of glaucoma and not short, sharp pressure spikes caused by things like blinking or swallowing.

In tests on rabbits, the team demonstrated that the closed-loop drug delivery enabled by the smart contact lens was as effective as conventional treatment with eye drops, while also enabling accurate monitoring of eye pressure. They also recorded no inflammation or other biocompatibility issues over 14 days of repeated use.

The new device is an elegant solution for combining pressure sensing and drug delivery while avoiding the comfort and safety concerns associated with the use of electronic components, says Chi Hwan Lee, a professor of biomedical and mechanical engineering at Purdue University. “These are important considerations for long-term wear and real-world adoption, particularly in a chronic condition such as glaucoma.”

However, Lee notes that ditching electronics comes with trade-offs including reduced precision and robustness. More importantly, the fact that pressure readings require the user to hold a smartphone up to their eye means that monitoring is not continuous. This is a significant limitation, he adds, as glaucoma often features transient spikes and fluctuations in eye pressure, often outside of waking hours, that are clinically relevant and would be missed by this system.

Nonetheless, Lee thinks the technology could be a useful complement to electronics-based approaches. “When used alongside electronic systems, they could offer a synergistic strategy—providing a simpler, low-cost, and potentially more comfortable option for intermittent or threshold-based monitoring and therapy, while electronic devices deliver continuous, high-resolution data,” he says.

One major advantage of the approach, says Zhu, is that the fabrication process is already well aligned with existing contact lens manufacturing approaches. And all of the information required to personalize the contact lenses for specific patients could be collected by clinicians during the standard process used to fit contact lenses.

Zhu and his collaborators are already working toward commercialization of the technology and have applied for a provisional patent. And while the focus is currently on glaucoma, he thinks the platform they’ve developed could ultimately be extended to a host of eye diseases, including dry eye, diabetic retinopathy, and age-related macular degeneration.

Scott Imbrie vividly remembers the first time he used a robotic arm to shake someone’s hand and felt the robotic limb as if it were his own. “I still get goosebumps when I think about that initial contact,” he says. “It’s just unexplainable.” The moment came courtesy of a brain implant: an array of electrodes that let him control a robotic arm and receive tactile sensations back to the brain.

Getting there took decades. In 1985, Imbrie had woken up in the hospital after a car accident with a broken neck and a doctor telling him he’d never use his hands or legs again. His response was an expletive, he says—and a decision. “I’m not going to allow someone to tell me what I can and can’t do.” With the determination of a head-strong 22-year-old, Imbrie gradually regained the ability to walk and some limited arm movement. Aware of how unusual his recovery was, the Illinois-native wanted to help others in similar situations and began looking for research projects related to spinal cord injuries. For decades, though, he wasn’t the right fit, until in 2020 he was finally accepted into a University of Chicago trial.

Scott Imbrie has shaken hands with a robotic arm controlled by a brain implant. The electrodes record neural signals that enable him to move the device and receive tactile feedback. Top: 60 Minutes/CBS News; Bottom: University of Chicago

Imbrie is part of a rarefied group: More people have gone to space than have received advanced brain-computer interfaces (BCI) like his. But a growing number of companies are now attempting to move the devices out of neuroscience labs and into mainstream medical care, where they could help millions of people with paralysis and other neurological conditions. Some companies even hope that BCIs will eventually become a consumer technology.

None of that will be possible without people like Imbrie. He’s a member of the BCI Pioneers Coalition, an advocacy group founded in 2018 by Ian Burkhart, the first quadriplegic to regain hand movement using a brain implant.

That life-changing experience convinced Burkhart that BCIs will make the leap from lab to real world only if users help shape the technology by sharing their perspectives on what works, what doesn’t, and how the devices fit into daily life. The coalition aims to ensure that companies, clinicians, and regulators hear directly from trial participants.

This article is part of the special report “Cyborg Tech From the Inside” →

The group also serves as a peer-support network for trial participants. That’s crucial, because despite the steady drumbeat of miraculous results from BCI trials, receiving a brain implant comes with significant risks. Surgical complications, such as bleeding or infection in the brain, are possible. Even more concerning is the potential psychological toll if the implant fails to work as expected or if life-changing improvements are eventually withdrawn.

Researchers spell this out upfront, and many are put off, says John Downey, an assistant professor of neurological surgery at the University of Chicago and the lead on Imbrie’s clinical trial. “I would say, the number of people I talk to about doing it is probably 10 to 20 times the number of people that actually end up doing it,” he says.

Ian Burkhart founded the BCI Pioneers Coalition to ensure that companies developing brain implants hear directly from the people using them. Left: Andrew Spear/Redux; Right: Ian Burkhart

What Happens in a BCI Trial?

BCI pioneers arrive at their unique status via a number of paths, including spinal cord injuries, stroke-induced paralysis, and amyotrophic lateral sclerosis (ALS). The implants they receive come from Blackrock Neurotech, Neuralink, Synchron, and other companies, and are being tested for restoring limb function, controlling computers and robotic arms, and even restoring speech.

Many of the implants record signals from the motor cortex—the part of the brain that controls voluntary movements—to move external devices. Some others target the somatosensory cortex, which processes sensory signals from the body, including touch, pain, temperature, and limb position, to re-create tactile sensation.

BCI Designs Used by Today’s Pioneers

BCI Designs

Ease of use depends heavily on the application. Restoring function to a user’s own limbs or controlling robotic arms involves the most difficult learning curve. In early sessions, participants watch a virtual arm reach for objects while they imagine or attempt the same movement. Researchers record related brain signals and use them to train “decoder” software, which translates neural activity into control signals for a robotic arm or stimulation patterns for the user’s nerves or muscles.

Paralyzed in a 2010 swimming accident, Burkhart took part in a trial conducted by Battelle Memorial Institute and Ohio State University from 2014 to 2021. His implant recorded signals from his motor cortex as he attempted to move his hand, and the system relayed those commands to electrodes in his arm that stimulated the muscles controlling his fingers.

Ian Burkhart, who is paralyzed from the chest down, received a brain implant that routed neural signals through a computer to his paralyzed muscles, enabling him to play a video game. Battelle

Getting the system to work seamlessly took time, says Burkhart, and initially required intense concentration. Eventually, he could shift his focus from each individual finger movement to the overall task, allowing him to swipe a credit card, pour from a bottle, and even play Guitar Hero.

Training a decoder is also not a one-and-done process. Systems must be regularly recalibrated to account for “neural drift”—the gradual shift in a person’s neural activity patterns over time. For complex tasks like robotic arm control, researchers may have to essentially train an entirely new decoder before each session, which can take up to an hour.

Austin Beggin says that testing a BCI is hard work, but he adds that moments like petting his dog make it all worth it. Daniel Lozada/The New York Times/Redux

Even after the system is ready, using the device can be taxing, says Austin Beggin, who was paralyzed in a swimming accident in 2015 and now participates in a Case Western Reserve University trial aimed at restoring hand movement. “The mental work of just trying to do something like shaking hands or feeding yourself is 100-fold versus you guys that don’t even think about it,” he says.

It’s also a serious time commitment. Beggin travels more than 2 hours from his home in Lima, Ohio, to Cleveland for two weeks every month to take part in experiments. All the equipment is set up in the house he stays in, and he typically works with the researchers for 3 to 4 hours a day. The majority of the experiments are not actually task-focused, he says, and instead are aimed at adjusting the control software or better understanding his neural responses to different stimuli.

But the BCI users say the hard work is worth it. Beyond the hope of restoring lost function, many feel a strong moral obligation to advance a technology that could help others. Beggin compares the pioneers to the early astronauts who laid the groundwork for the lunar landings. “We’re some of the first astronauts just to get shot up for a couple of hours and come back down to earth,” he says.

The Emotional Impact of BCIs

Speak to BCI early adopters and a pattern emerges: The biggest benefits are often more emotional than practical. Using a robotic arm to feed oneself or control a computer is clearly useful, but many pioneers say the most meaningful moments are the ones the experiment wasn’t even trying to produce. Beggin counts shaking his parents’ hands for the first time since his injury and stroking his pet dachshund as among his favorite moments. “That stuff is absolutely incredible,” he says.

Neuralink participant Alex Conley, who broke his neck in a car accident in 2021, uses his implant to control both a robotic arm and computers, enabling him to open doors, feed himself, and handle a smartphone. But he says the biggest boost has come from using computer-aided design software.

A former mechanic, Conley began using the software within days of receiving his implant to design parts that could be fabricated on a 3D printer. He has designed everything from replacement parts for his uncle’s power tools to bumpers for his brother-in-law’s truck. “I was a very big problem solver before my accident, I was able to fix people’s things,” he says. “This gives me that same little burst of joy.”

BCI user Nathan Copeland used a robotic arm to get a fist bump from then-President Barack Obama in 2016.Jim Watson/AFP/Getty Images

The outside world often underestimates those little wins, says Nathan Copeland, who holds the record for the longest functional brain implant. After breaking his neck in a car accident in 2004, he joined a University of Pittsburgh BCI trial in 2015 and has since used the device to control both computers and a robotic arm.

After he uploaded a video to Reddit of himself playing Final Fantasy XIV, one commenter criticized him for not using his device for more practical tasks. Copeland says people don’t understand that those lighthearted activities also matter. “A lot of tasks that people think are mundane or frivolous are probably the tasks that have the most impact on someone that can’t do them,” he says. “Agency and freedom of expression, I think, are the things that impact a person’s life the most.”

Nathan Copeland plays Final Fantasy XIV using his brain implant to control the game character.

When Brain Implants Become Life-Changing

This perspective resonates with Neuralink’s first user, Noland Arbaugh—paralyzed from the neck down after a swimming accident in 2016. After receiving his implant in January 2024, he was able to control a cursor within minutes of the device being switched on. A few days later, the engineers let him play the video game Civilization VI, and the technology’s potential suddenly felt real. “I played it for 8 hours or 12 hours straight,” he says. “It made me feel so independent and so free.”

Before receiving his Neuralink implant, Noland Arbaugh used mouth-operated devices to control a computer. He says the BCI is more reliable and enables him to do many more things on his own. Rebecca Noble/The New York Times/Redux

But the technology is also providing more practical benefits. Before his implant, Arbaugh relied on a mouth-held typing stick and a mouth-controlled joystick called a Quadstick, which uses sip-or-puff sensors to issue commands. But the fiddliness of this equipment required constant caregiver support. The Neuralink implant has dramatically increased the number of things he can do independently. He says he finds great value in not needing his family “to come in and help me 100 times a day.”

For Casey Harrell, the technology has been even more transformative. Diagnosed with ALS in 2020, the climate activist had just welcomed a baby daughter and was in the midst of a major campaign, pressuring a financial firm to divest from companies that had poor environmental records.

Casey Harrell was able to communicate again within 30 minutes of his BCI being switched on. The device translates his neural signals quickly enough for him to hold conversations. Ian Bates/The New York Times/Redux

“Every morning we’d wake up and there’d be a new thing he couldn’t do, a new part of his body that didn’t work,” says his wife, Levana Saxon. Most alarming was his rapid loss of speech, which, among other things, left him unable to indicate when he was in pain. Then a relative alerted him to a clinical trial at the University of California, Davis, using BCIs to restore speech. He immediately signed up.

The device, implanted in July 2023, records from the brain region that controls muscles involved in talking and translates these signals into instructions for a voice synthesizer. Within 30 minutes of it being switched on, Harrell could communicate again. “I was absolutely overwhelmed with the thought of how this would impact my life and allow me to talk to my family and friends and better interact with my daughter,” he says. “It just was so overwhelming that I began to cry.”

While earlier assistive technology limited him to short, direct commands, Harrell says the BCI is fast enough that he can hold a proper conversation, and he’s been able to resume work part-time.

What’s Holding BCI Technology Back?

BCI technology still has limits. Most trial participants using Blackrock Neurotech implants can operate their devices only in the lab because the systems rely on wired connections and racks of computer hardware. Some users, including Copeland and Harrell, have had the equipment installed at home, but they still can’t leave the house with it. “That would be a big unlock if I was able to do so,” says Harrell.

The academic nature of many trials creates additional constraints. Pressure to publish and secure funding pushes researchers to demonstrate peak performance on narrow tasks rather than build more versatile and reliable systems, says Mariska Vansteensel, who runs BCI studies at the University Medical Center Utrecht in the Netherlands. She says that investigating the technology’s limits or repeating an experiment in new patients is “less rewarded in terms of funding.”

In a clinical trial, Scott Imbrie uses a BCI to control a robotic arm, using signals from his motor cortex to make it move a block. University of Chicago

One of Imbrie’s biggest frustrations is the rapid turnover in experiments. Just as he begins to get proficient at one task, he’s asked to switch to the next task. Study designs also mean that much of the users’ time is spent on mundane tasks required to fine-tune the system.

Perhaps the biggest issue is that trials are often time-limited. That’s partly because scar tissue from the body’s immune response to the implant can gradually degrade signal quality. But constraints on funding and researcher availability can also make it impossible for users to keep using their BCIs after their trials end, even when the technology is still functional.

Ian Burkhart’s BCI enables him to grasp objects, pour from a bottle, and swipe a credit card.

Burkhart has firsthand experience. His trial was extended, but the implant was eventually removed after he got an infection. He always knew the trial would end, but it was nonetheless challenging. “It was a little bit of a tease where I got to see the capability of the restoration of function,” he says. “Now I’m just back to where I was.”

The Push to Commercialize BCIs

Progress is being made in transitioning the technology from experimental research devices to fully-fledged medical products that could help users in their everyday lives. Most academic BCI research has relied on Blackrock Neurotech’s Utah Arrays, which typically feature 96 needlelike electrodes that penetrate the brain’s surface. The implant is connected to a skull-mounted pedestal that’s wired to external hardware. But some of the newer devices are sleeker and less invasive.

Neuralink’s implant houses its electronics and rechargeable battery in a coin-size unit connected to flexible electrode threads inserted into the brain by a robotic “sewing machine.” The implant, which is roughly the size of a quarter or a euro, is mounted in a hole cut into the skull and charges and transfers data wirelessly. Synchron takes a different approach, threading a stent-like implant through blood vessels into the motor cortex. This “stentrode” connects by wire to a unit in the chest that powers the implant and transmits data wirelessly.

Rodney Gorham can use his Synchron implant to control not just a computer, but also smart devices in his home like an air conditioner, fan, and smart speaker. Rodney Decker

Neuralink’s decoder runs on a laptop, while Synchron deploys a smartphone-size signal processing unit as a wireless bridge to the user’s devices, which allows them to use their implants at home and on the move. The companies have also developed adaptive decoders that use machine learning to adjust to neural drift on the fly, reducing the need for recalibration.

Making these devices truly user-friendly will require technology that can interpret user context, says Kurt Haggstrom, Synchron’s chief commercial officer—including mood, attention levels, and environmental factors like background noise and location. This approach will require AI that analyzes neural signals alongside other data streams such as audio and visual input.

Last year, Synchron took a first step by pairing its implant with an Apple Vision Pro headset. When trial participant Rodney Gorham looked at devices such as a fan, a smart speaker, and an air conditioner, the headset overlaid a menu that enabled him to adjust the device’s settings using his implant.

Rodney Gorham uses his Synchron implant to turn on music, feed his dog, and more. Synchron BCI

Another way to reduce cognitive load is to detect high-order signals of intent in neural data rather than low-level motor commands, says Florian Solzbacher, cofounder and chief scientific officer of Blackrock Neurotech. For instance, rather than manually navigating to an email app and typing, the user could simply think about sending an email and the system would then open it with content already prepopulated, he says.

Durability may prove a thornier problem to solve, UChicago’s Downey says. Current implants last around a decade—well short of a lifelong solution. And with limited real estate in the brain, replacement is only possible once or twice, he says.

Rapid technological progress also raises difficult decisions about whether to get a BCI implant now or wait for a more advanced device. This was a major concern for Gorham’s wife, Caroline. “I was hesitant. I didn’t want him to go on the trial but maybe a future one,” she says. “It was my fear of missing out on future upgrades.”

Will Brain Implants Ever Become Consumer Tech?

Some executives have raised the prospect of BCIs eventually becoming consumer devices. Neuralink founder Elon Musk has been particularly vocal, suggesting that the company’s implants could replace smartphones, let people save and replay memories, or even achieve “symbiosis” with AI.

This kind of talk inspires mixed feelings in users. The hype brings visibility and funding, says Beggin, but could divert attention from medical users’ needs. Copeland worries that consumer branding could strip the devices of insurance coverage and that rising demand may make it harder to access qualified surgeons.

Noland Arbaugh, the first recipient of Neuralink’s BCI, says that using the implant to control a computer made him feel independent and free. Steve Craft/Guardian/eyevine/Redux

There are also concerns about how data collected by BCI companies will be handled if the devices go mainstream. As a trial participant, Arbaugh says he’s comfortable signing away his data rights to advance the technology, but he thinks stronger legal protections will be needed in the future. “Does that data still belong to Neuralink? Does it belong to each person? And can that data be sold?” he asks.

Blackrock’s Solzbacher says the company remains focused on the medical applications of the technology. But he also believes it is building a “universal interface to any kind of a computerized system” that may have broader applications in the future. And he says the company owes it to users not to limit them to a bare-bones assistive technology. “Why would somebody who’s got a medical condition want to get less than something that somebody who’s able-bodied would possibly also take?” says Solzbacher.

The ever-optimistic Imbrie heartily agrees. Medical devices are invariably expensive, he says, but targeting consumer applications could push companies to keep devices simple and affordable while continuing to add features. “I truly believe that making it a consumer-available product will just enhance the product’s capabilities for the medical field,” he says.

Imbrie is on a mission to refocus the conversation around BCIs on the positives. While concerns about risks are valid, he worries that the alarming language often used to describe brain implants discourages people from volunteering for trials that could help them.

“I remember laying there in the bed and not being able to move,” he says, “and it was really dehumanizing having to ask someone to do everything for you. As humans, we want to be independent.”

This article appears in the May 2026 print issue as “Life With an Experimental Brain Implant.”

By many estimates, quantum computers will need millions of qubits to realize their potential applications in cybersecurity, drug development, and other industries. The problem is, anyone who has wanted to simultaneously control millions of a certain kind of qubit has run into the problem of trying to control millions of laser beams.

That’s exactly the challenge that was faced by scientists working on the MITRE Quantum Moonshot project, which brought together scientists from MITRE, MIT, the University of Colorado at Boulder, and Sandia National Laboratories. The solution they developed came in the form of an image projection technology that they realized could also be the fix for a host of other challenges in augmented reality, biomedical imaging, and elsewhere. The device is a 1-square-millimeter photonic chip capable of projecting the Mona Lisa onto an area smaller than the size of two human egg cells.

“When we started, we certainly never would have anticipated that we would be making a technology that might revolutionize imaging,” says Matt Eichenfield, one of the leaders of the Quantum Moonshot project, a collaborative research effort focused on developing a scalable, diamond-based quantum computer, and a professor of quantum engineering at the University of Colorado at Boulder. Each second, their chip is capable of projecting 68.6 million individual spots of light—called scannable pixels—to differentiate them from physical pixels. That’s more than 50 times the capability of previous technology, such as micro-electromechanical systems (MEMS) micromirror arrays.

“We have now made a scannable pixel that is at the absolute limit of what diffraction allows,” says Henry Wen, a visiting researcher at MIT and a photonics engineer at QuEra Computing.

The chip’s distinguishing feature is an array of tiny microscale cantilevers, which curve away from the plane of the chip in response to voltage and act as miniature “ski jumps” for light. Light is channeled along the length of each cantilever via a waveguide and exits at its tip. The cantilevers contain a thin layer of aluminum nitride, a piezoelectric that expands or contracts under voltage, thus moving the micromachine up and down and enabling the array to scan beams of light over a two-dimensional area.

Despite the magnitude of the team’s achievement, Eichenfield says that the process of engineering the cantilevers was “pretty smooth.” Each cantilever is composed of a stack of several submicrometer layers of material and curls approximately 90 degrees out of the plane at rest. To achieve such a high curvature, the team took advantage of differences in the contraction and expansion of individual layers caused by physical stresses in the material resulting from the fabrication process. The materials are first deposited flat onto the chip. Then, a layer in the chip below the cantilever is removed, allowing the material stresses to take effect, releasing the cantilever from the chip and allowing it to curl out. The top layer of each cantilever also features a series of silicon dioxide bars running perpendicular to the waveguide, which keep the cantilever from curling along its width while also improving its lengthwise curvature.

A micro-cantilever wiggles and waggles to project light in the right place.Matt Saha, Y. Henry Wen, et al.

What was more of a challenge than engineering the chip itself was figuring out the details of actually making the chip project images and videos. Working out the process of synchronizing and timing the cantilevers’ motion and light beams to generate the right colors at the right time was a substantial effort, according to Andy Greenspon, a researcher at MITRE who also worked on the project. Now, the team has successfully projected a variety of videos from a single cantilever, including clips from the movie A Charlie Brown Christmas.

The chip projected a roughly 125-micrometer image of the Mona Lisa.Matt Saha, Y. Henry Wen, et al.

Because the chip can project so many more spots in any given time interval than any previous beam scanners, it could also be used to control many more qubits in quantum computers. The Quantum Moonshot program’s mission is to build a quantum computer that can be scaled to millions of qubits. So clearly, it needs a scalable way of controlling each one, explains Wen. Instead of using one laser per qubit, the team realized that not every qubit needed to be controlled at every given moment. The chip’s ability to move light beams over a two-dimensional area would allow them to control all of the qubits with many fewer lasers.

Another process that Wen thinks the chip could improve is scanning objects for 3D printing. Today, that typically involves using a single laser to scan over the entire surface of an object. The new chip, however, could potentially employ thousands of laser beams. “I think now you can take a process that would have taken hours and maybe bring it down to minutes,” says Wen.

Wen is also excited to explore the potential of different cantilever shapes. By changing the orientations of the bars perpendicular to the waveguide, the team has been able to make the cantilevers curl into helixes. Wen says that such unusual shapes could be useful in making a lab-on-a-chip for cell biology or drug development. “A lot of this stuff is imaging, scanning a laser across something, either to image it or to stimulate some response. And so we could have one of these ski jumps curl not just up, but actually curl back around, and then move around and scan over a sample,” Wen explains. “If you can imagine a structure that will be useful for you, we should try it.”

Identifying bacteria by sight can be quite difficult. Why not listen to them instead?

Researchers at TU Delft in the Netherlands and the university’s spinoff company SoundCell think that bacterial infections could be diagnosed with sound. They’ve crafted a nanoscale drum kit that uses some of the world’s smallest percussion instruments to turn a bacterium’s motions into song.

Previously, the Delft researchers showed that listening to a germ’s drumbeat could quickly screen it for antibiotic resistance. Now, the same researchers have discovered that different bacteria play different sounds on the drum. They’ve shown they can identify a bacterium from its song alone.

“We can really look at the level of a single cell,” says Farbod Alijani, a mechanical engineer at TU Delft and one of the authors of a new paper. “We have that sensitivity.” Alijani and colleagues reported their latest findings this March in ACS Sensors.

How to Build the World’s Tiniest Drum

The Delft researchers call their instrument of choice a “nanodrum.” Its drumhead is fashioned from two graphene sheets, less than 1 nanometer thick, laid atop an 8 micrometer-wide cavity. This size fits most bacteria, which are about one to 10 micrometers in length.

Several years ago, the Delft researchers noticed that, if a living bacterium settled on the graphene sheet, it would beat a pattern on the drumhead. They were detecting the life-form’s subtle motions, such as the whirling of the propellor-like flagellum the bacterium uses to move about. When the drumhead moved, it left signals on a beam of laser light reflected off the surface, allowing the researchers to record the bacterium’s motion.

The drum’s tiny size is key to pinpointing individual bacteria. The Delft researchers were not the first to capture bacteria in motion, but older methods usually had to average the movements of an ensemble of many bacteria because of their microscale. By comparison, each graphene drumhead is small enough to isolate—and record—a single bacterium.

Graphene is key to this instrument’s construction. The material is both strong enough to support a bacterium’s weight and sensitive enough to bend with each subtle bounce on the drum.

Then, by converting its drumbeat to a soundtrack, it’s possible to literally hear the motions of a living bacterium. “It’s very noisy, like a wind tunnel,” says Aleksandre Japaridze, SoundCell’s chief technical officer, who is also an author of the paper.

By contrast, “if you kill it with a drug, it’s immediately very silent, and you don’t hear anything.” In previously published work, when the researchers pumped an antibiotic onto drums played by E. coli, the drums fell quiet within hours. But when they did the same to E. coli they knew to be antibiotic-resistant, the bacteria played on, seemingly unaffected.

From One Song to Many

Over the following years, the Delft researchers refined their technology’s ability to screen bacteria for antibiotic resistance. Let a patient’s bacteria play the drums, then administer a given antibiotic—if the music stops, that antibiotic should work.

But their work took an unexpected turn after an attendee at a conference asked Alijani if different bacteria made different sounds. Unsure of the answer, the researchers wondered how they could find out.

It was clearly possible to tell a living bacterium from a dead one by listening alone, but separating one bacterium from another species required a more sophisticated approach. The Delft researchers recorded the drumbeats of different infectious bacteria from real patients’ samples. Instead of using raw sounds, the researchers processed them into time-frequency spectrograms, which allowed the researchers to more carefully study the patterns of each bacterium’s motion.

The researchers trained two different machine learning models to examine a spectrogram and identify its drummer as one of three species: E. coli, Staphylococcus aureus (responsible for staph infections), or Klebsiella pneumoniae (one of the germs that can cause pneumonia).

Both models, each with a different underlying architecture, scored high marks in testing: One classified bacteria with 87 percent accuracy, and the other achieved 88 percent. These results suggest that each species plays different characteristic notes when it moves on the drum.

“It’s a completely different way of interpreting the different species,” Japaridze says. “Not chemically or biologically, with markers and genes, but just purely on...mechanical behavior.”

Diagnosis Through Song and Dance

The Delft researchers think their drums are a powerful diagnostic tool. SoundCell was originally spun off to commercialize the ability to quickly and easily determine whether a bacterium is resistant to a given antibiotic, and the researchers hope hospitals in the future will also listen to the songs of a patient’s sample to identify the infection.

Antimicrobial-resistant germs may be responsible for more than 1 million deaths each year and may play a part in millions more. There are many reasons that antibiotic-resistant bacteria are potent threats—one is that the tests for whether a microbe is resistant are relatively slow. Today’s tools may take days to report if a microbe is resistant to a given antibiotic. By comparison, SoundCell’s technology can do this in as little as an hour.

First, SoundCell must show its nanodrums can work in the hospital. The Delft researchers’ published work was conducted on a bulky apparatus on an optical table, within the controlled confines of a laboratory. So, SoundCell has repackaged its nanodrums into a smaller device better suited for hospital use.

SoundCell has now deployed this device at two hospitals in the Netherlands, Japaridze says, to verify their performance.

This article appears in the June 2026 print issue as ‘“Nanodrums” Identify Bouncing Bacteria.’

Cells that have been genetically engineered to produce drugs are a promising way to deliver medicines inside the human body, but keeping those cells alive is challenging. A new bioelectronic implant can now support populations of three different drug-producing cells for more than a month. The researchers behind the result say it’s a step toward “living pharmacies” that can deliver a range of drugs on demand.

But another promising avenue involves using genetic engineering to turn cells into living drug factories that can pump out a class of medicines known as “biologics”—drugs derived from living organisms. The U.S. Food and Drug Administration has approved biologics targeting a wide range of conditions, including various cancers, autoimmune diseases like arthritis and psoriasis, and chronic conditions like asthma and Crohn’s disease.

For the approach to work, the cells need to stay alive long enough in the host’s body to produce the correct dose of the medicine. One of the biggest barriers is ensuring that the cells receive enough oxygen to thrive. The multi-institution team behind the latest development has created a bioelectronic implant the size of a thumb drive that houses drug-producing cells and also uses electrochemical reactions to provide a reliable supply of oxygen to them.

Bioelectronic Implant Extends Cell Survival

In a recent paper in Device, the team showed the implant could sustain three different strains of engineered cells for 31 days when implanted in rats, providing steady production of multiple drugs. While the implant remains a proof-of-concept, the long-term goal is to create a device that can control the timing and dosage of multiple therapies over extended periods, says Jonathan Rivnay, a professor of biomedical engineering at Northwestern University.

“Imagine a device that’s a few millimeters that you can put under your skin, and it can serve this purpose of a multi-therapeutic living pharmacy that can last for months to years,” Rivnay says. “That would be game-changing. I think we have a long way to go, but the kind of advances that we’re writing about in this article are laying the foundation for what that might look like.”

Access to oxygen is the main limitation for this kind of cell therapy, says Omid Veiseh, a professor of bioengineering at Rice University. The area directly under the skin, which is an attractive target for implants because it can be accessed using minimally invasive procedures, tends to be particularly poorly oxygenated.

One potential solution are electrochemical approaches that convert water into oxygen and hydrogen. But these approaches have primarily been developed for industrial applications that don’t translate well to the constraints of operating inside the body or using its water. Specifically, they have high power requirements and potentially produce toxic by-products like chlorine and hydrogen peroxide.

Previous research from the same researchers demonstrated a device that used a thin film of iridium oxide as a catalyst to generate oxygen, which enabled it to run at voltages between 1.6 and 1.9 volts (lower than other electrochemical reactions) and minimized the creation of harmful by-products. But the device still required an external power source.

HOBIT Wireless Oxygenation Implant System

Building on that work, the researchers have now built a device they call HOBIT (Hybrid Oxygenation Bioelectronics system for Implanted Therapy) that integrates an oxygenator, a chamber for housing cells, a wireless communication system to control oxygen production and transmit data, and an internal battery into a hermetically sealed implant just 4.5 centimeters long.

“I think the power is in the fully implantable nature of this platform,” says Chris Wright, a Ph.D. student at Rice. “You don’t need external power, you don’t need external devices that connect to it. That’s a big differentiator.”

The cells are encapsulated in permeable gel capsules that allow nutrients and drugs to pass through, but prevents cells from escaping or being attacked by the body’s immune system.

The device is able to house drug-producing cells at a density as high as 60 million per milliliter. That density allowed the researchers to load three different engineered cell strains designed to produce an anti-HIV antibody, a hormone that regulates metabolism, and a peptide similar to the weight loss drug GLP-1.

These drugs all last different amounts of time in the body, but by balancing the ratios of the cells and controlling the oxygen supply, the researchers were able to maintain steady production of each drug therapy for 31 days. By the end of the trial, 64.6 percent of cells were still viable, compared to just 19.2 percent in a control device without an oxygenator.

This ability to produce several drugs at reliable levels over extended periods could significantly reduce the burden of administering complex multi-therapy treatment regimes, says Veiseh. The team is already working to apply the technology as part of a project funded by the Advanced Research Projects Agency for Health (ARPA-H) called THOR (Targeted Hybrid Oncotherapeutic Regulation), which will produce multiple cancer-fighting immunotherapies with different half-lives in the abdomen.

Rivnay says that they hope to one day augment the device with sensors that can detect various biomarkers, as well as ways to control drug production using optogenetics and electrogenetics—methods for altering the genetic activity of cells using flashes of light or pulses of electricity, respectively. “All of those things layer onto a more complex living-pharmacy-type system, building that longer term vision of not only controlling dose but controlling exactly when you supply a dose,” he says.

One outstanding challenge will be getting approval from the FDA—the agency has yet to sanction a biohybrid device that combines both living and non-living components. But Rivnay remains confident that with the right approach, they can win over regulators.

“It’s just a matter of showing that it’s safe and showing that it’s effective,” he says. “That’s why we have to start relatively simple and not throw all the bells and whistles at it straightaway.”

Abhishek Appaji has committed his career to bringing lifesaving technology to underresourced communities. The IEEE senior member weaves together artificial intelligence, biomedical engineering, deep learning, and neuroscience to make doctors’ jobs easier and to improve patient outcomes.

“The intersection of these fields is where the most impactful breakthroughs in diagnostic precision occur,” says Appaji, an associate professor of medical electronics engineering at the B.M.S. College of Engineering, in Bengaluru, India.

Abhishek Appaji

Employer

B.M.S. College of Engineering, in Bengaluru, India

Job title

Associate professor of medical electronics engineering

Member grade

IEEE senior member

Alma maters

B.M.S. College of Engineering; University of Visvesvaraya, in Bengaluru; Maastricht University, in the Netherlands

Many of his inventions have been deployed in remote areas of India, providing physicians with quality diagnostic tools, including an AI-powered machine that can scan retinas to detect medical conditions and a smart bed that continuously monitors a patient’s vital signs.

An active volunteer with the IEEE Young Professionals Bangalore Section, he has launched professional networking events, technology workshops, a mentorship program, and other initiatives.

For his “contributions to accessible AI-driven health care solutions and leadership in empowering young professionals,” Appaji is the recipient of this year’s IEEE Theodore W. Hissey Outstanding Young Professional Award. The honor is sponsored by the IEEE Photonics and Power & Energy societies as well as IEEE Young Professionals. The award is scheduled to be presented this month during the IEEE Honors Ceremony in New York City.

“This award represents a significant milestone in my career,” Appaji says. “It validates my core belief that our success as engineers is not solely measured by research outcomes or publications but by the tangible impact we have on lives through accessible technology and the quality of the next generation of leaders we empower.”

Developing a blood glucose measurement device

After earning a bachelor’s degree in engineering from B.M.S. in 2010, he joined the school as a lecturer in its medical electronics engineering department. At the same time, he pursued master’s degrees in bioinformatics at the University Visvesvarya College of Engineering, also in Bengaluru. He graduated in 2013 and continued to teach at B.M.S.C.E.

Four years later, Appaji signed up for the MIT Global Entrepreneurship Bootcamp, a two-week intensive hybrid program that includes webinars, online courses, and a five-day stay at MIT. It’s designed to give teams of aspiring entrepreneurs, innovators, and early-stage founders the structured mindset, tools, and frameworks they need to succeed.

Appaji says he discovered the program while researching opportunities in innovation.

“I had the technical expertise, but I needed a structured framework to transition my research from the laboratory to the market,” he says.

During the MIT boot camp, he and a team of four other participants were tasked with approaching a complex health care challenge. They developed a noninvasive blood glucose measurement device to manage gestational diabetes—a condition that causes high blood sugar and insulin resistance during pregnancy. When the program ended, Appaji and two of his Australia-based teammates continued their collaboration by founding Glucotek in Brisbane, Australia.

Inspired to continue his research in health care technology, Appaji pursued a doctorate in mental health and neurosciences at Maastricht University, in the Netherlands.

His thesis focused on computational methods to identify retinal vascular patterns.

“The patterns we analyze—including the curvature of the vessels, the angles at which they branch out, and their dimensions—reveal the health of the microvascular system,” he says. “With conditions like schizophrenia and bipolar disorder, microvascular changes mirror neurovascular changes in the brain.”

“My journey has shown me that IEEE is much more than a professional society; it is a global platform that allows me to collaborate with a diverse network of experts to solve local humanitarian challenges.”

Examining and measuring the retinal vascular system offers physicians a noninvasive way to examine neural changes, which can be biomarkers for psychiatric illnesses, he says.

To bring his idea to life, he collaborated with an ophthalmologist, a psychiatrist, and colleagues from his engineering school to develop a screening device. They also created and trained the AI models that analyze retinal images.

Ideas from his thesis led to the creation of the Smart Eye Kiosk, an AI-powered tool that scans the network of small veins that deliver blood to the inner retina. The tool monitors stress levels and mental health. It also screens for basic eye diseases such as diabetic retinopathy, as well as damage to retinal blood vessels caused by high blood sugar.

Retinal images also can reveal physiological changes in the brain associated with psychiatric disorders such as schizophrenia and bipolar disorder, Appaji says. The kiosk uses AI models to analyze measurements of the vasculature network, such as vessel thickness, which can be biomarkers for psychiatric conditions. Since mental illnesses can be linked to genetics, relatives of patients with schizophrenia and bipolar disorder were also invited to participate in a study funded by India’s Cognitive Science Research Initiative’s Department of Science & Technology. The clinical data from this study can pave the way for earlier, more accurate diagnoses.

“The biological basis for this is fascinating,” Appaji says. “The retina is the only place in the human body where the central nervous system and the vascular system can be visualized directly and noninvasively. Anatomically, the retina is an extension of the posterior part of the brain. Therefore, physiological changes in the brain are often reflected in the eyes.”

This kiosk was developed in collaboration with Tan Tock Seng Hospital and Nanyang Technological University, which was funded by Ng Teng Fong Healthcare Innovation Program.

He earned his Ph.D. in 2020 from Maastricht, and he received the Best Thesis Award from the university’s Mental Health and Neuroscience Research Institute. Appaji credits his time at the school for his multidisciplinary approach to developing medical devices.

“Having the perspectives of mentors from diverse fields was essential to help me move my research beyond theory into a data-driven diagnostic tool,” he says.

He was then named institutional coordinator of R&D at B.M.S. and later was promoted to be its head.

Abhishek Appaji working on a smart bed sensor that continuously monitors a patient’s vital signs without the use of wires or wearable sensors.Abhishek Appaji

A wireless smart bed to monitor vital signs

Appaji continues to develop technologies for patients who need them most. “I feel a deep need to bridge this gap and ensure innovations have a tangible impact on society,” he says. In addition to the Smart Eye Kiosk, he improved the performance of the sensors of the smart beds that continuously monitor a patient’s vital signs without the use of wires or wearable sensors. The beds help hospital staff check on their patients in a noninvasive way.

The project was done in collaboration with health AI company Dozee (Turtle Shell Technologies) in Bengaluru. The system measures mechanical microvibrations produced by the body in response to the ejection of blood into the aorta, which occurs with each heartbeat. A thin, industrial-grade sensor sheet is placed underneath the mattress. Additional funding is being provided by India’s Department of Science and Technology.

“These sensors are incredibly sensitive,” Appaji says. “They pick up minute mechanical tremors through the mattress material.”

The sensors detect the force of the patient’s heartbeat and the expansion and contraction of their chest during respiration. The vibrations are converted into electrical signals and analyzed using deep learning algorithms developed by Appaji and his team at the university in collaboration with Dozee.

The technology is used in more than 200 hospitals throughout India and in thousands of households, he says.

Mentoring budding entrepreneurs

Appaji is also executive director of the BMSreenivasiah Innovators Guild Foundation, dedicated to nurturing entrepreneurial talent among students and faculty across the BMS group of Institutions. A not-for-profit company promoted by the BMS Education Trust, BIG Foundation provides a structured ecosystem for innovation, incubation, and startup growth.

There, Appaji mentors budding entrepreneurs, offering advice on business plans, product pitches, marketing strategies, and licensing. Participants are students and faculty members.

The foundation has incubated more than 10 ventures, according to Appaji.

“The majority are centered on health care applications,” he says, “and have successfully secured backing from investors and seed funds.”

Taking IEEE’s mission to heart

Appaji was introduced to IEEE as an undergraduate when one of his professors encouraged him to volunteer for a conference sponsored by the IEEE Engineering in Medicine and Biology Society. He transcribed the seminars for session chairs, assisted with managing the talks, and helped answer attendees’ questions.

“That experience was transformative,” he recalls. “I was amazed to find myself in the same room with the speakers and scientists who had authored the very textbooks I was studying.

“It was then that I realized IEEE is far more than just technology and volunteering; it is a global platform for high-level networking with world-class scientists and technologists.”

Appaji has served in several IEEE leadership positions, including 2018–2019 chair of the Young Professionals Bangalore Section. He is now treasurer of the IEEE Education Society, chair of IEEE Computer Society Bangalore Chapter, member of the steering committee of IEEE DataPort, and serves on the IEEE Member and Geographic Activities and IEEE Educational Activities boards.

“What motivates me to remain active within IEEE is the profound alignment between my personal goals and the organizational mission of advancing technology for the benefit of humanity,” he says. “My journey has shown me that IEEE is much more than a professional society; it is a global platform that allows me to collaborate with a diverse network of experts to solve local humanitarian challenges.”

The organization has helped fund some of Appaji’s lifesaving work. During the COVID-19 pandemic, he received a grant from the IEEE Humanitarian Technologies Board and Region 10 to develop 3D-printed protective equipment for people in Bengaluru’s underserved communities. The virus spread quickly in the high-density areas, where social distancing was nearly impossible. The kits, which included a door opener to avoid high-touch surfaces and an elbow-operated soap dispenser, were sent to nearly 500 households.

“This work remains one of my most meaningful contributions to humanitarian technology,” Appaji says, “demonstrating how engineering can be rapidly deployed to protect vulnerable populations during a global crisis.”

He advises younger IEEE members to: “Say yes to taking on roles of responsibility. Don’t wait for a formal title to lead; instead, start by volunteering to do small, manageable tasks within your local chapter or section.”

“The networking opportunities and leadership skills you gain through these early responsibilities will shape your professional career far more than any textbook ever could.”

Engineers have long tried to mimic life. They’ve built machine learning algorithms modeled after the human brain, designed machines that walk like dogs or fly like insects, and taught robots to adapt, however clumsily, to the world around them.

Now they are skipping imitation altogether.

Instead of taking inspiration from biology, they are building robots out of it: fashioning tiny, free-swimming assemblages of living cells that organize into self-directed systems, complete with neurons that wire themselves into functional circuits.

The result, reported last month in Advanced Science, is what the researchers call a “neurobot.”

These living machines could help scientists better understand how simple neural networks give rise to complex behaviors, a foundational step toward building cyborg systems that integrate biological tissue with engineered control. And with further refinement, they could be put to use in applications ranging from precision tissue repair to environmental cleanup.

“My general reaction is, ‘Wow, this is amazing!’ ” says Kate Adamala, a synthetic biologist at the University of Minnesota Twin Cities, who was not involved in the research. “This truly puts the engineering component into bioengineering.”

Toward Internal Control

Neurobots mark the latest advance in a series of increasingly sophisticated biological machines developed by Tufts University biologist Michael Levin and his collaborators.

First described in 2020, these clusters of living cells, when removed from their normal developmental context and cultured in simple saline conditions, spontaneously self-organize in such a manner that they move and act in novel ways. Under the microscope, they look like irregular, translucent blobs of tissue, but their coordinated motion reveals an emergent order that is unlike anything found in the natural world.

“These things don’t occur naturally,” says Carlos Gershenson, a computer scientist at Binghamton University, State University of New York, who studies artificial life and complex systems but was not involved in the neurobot research. “They’re made with natural cells, but we’re the ones arranging them.”

The earliest examples of this technology, called xenobots, were built from frog-derived tissues and mainly from a single type of structural cell. Despite the simplicity of their construction, however, they could propel themselves through water using beating hair-like projections called cilia. They survived for days without added nutrients. And they could repair minor damage, all without any scaffolding materials or genetic manipulation. Some could even self-replicate by spontaneously sweeping up loose stem cells.

Still, for all the novelty of these biological machines, their behavior was essentially mechanical. Their movements were driven by anatomy and physics, not by anything resembling internal control. They could sense chemical cues, change direction accordingly, and even retain traces of past experiences, as detailed in a preprint posted 17 March on bioRxiv.

But many other simple organisms—fungi, protists, and bacteria included—can do much the same. To achieve more flexible, coordinated behavior, they would need a way to integrate information across the body and dynamically direct their actions. Neurobots begin to provide that missing layer of control.

Small tufts of hairlike cilia, combined with the neurobot’s nervous system, allow it to move on its own. Haleh Fotowat

Linking Neural Activity to Action

Like earlier xenobots, neurobots are still built from frog cells, but they are now endowed with neurons that mature from partially differentiated stem cells. These nerve cells develop alongside structural tissues, forming branching connections throughout the autonomous beings. This means they can relay electrochemical signals from cell to cell.

And unlike other laboratory models of the nervous system—brain organoids, say, or lab-on-a-chip technologies—neurobots move. They swim, explore, and respond to their surroundings in ways that tie electrical signaling to observable movement, producing patterns of physical activity distinct from their non-neural counterparts.

Neurobots spend less time idling and more time exploring. They also trace looping and spiraling paths rather than repeating simple trajectories. And they respond differently to neuroactive drugs.

If the organizing principles that enable these internally guided motions and reflexes can now be deciphered, they could then be harnessed to produce more predictable biological functions, says Haleh Fotowat, a neuroengineer from Harvard’s Wyss Institute for Biologically Inspired Engineering, who collaborated with Levin’s team on the study.

“We’re still very early in terms of understanding the system and its capabilities.” But once the scientists understand how the neurobots self-organize, she says, “then we can begin to engineer on top of that.”

Beyond the practical, neurobots also raise deeper epistemological questions about the nature of biological organization, notes Levin. “Where does form and function come from in the first place?” he asks. “When it’s not evolved and it’s not engineered, where do these patterns come from?”

“This is a model system for asking those kinds of questions,” Levin says—in frog and human constructs alike.

From Discovery to Deployment

Among the many variations on the biobot theme are “anthrobots,” built from clusters of human lung cells instead of frog tissue.

Levin’s team now plans to add human neural cells to their anthrobots, extending the neurobot framework into a fully human context. Then, through further conditioning and guided learning, these living machines—like dogs trained to sniff for bombs—may become capable of adapting their behavior in predictable ways.

“The hope would be that you could teach them or train them to do what you want them to do,” says Josh Bongard, a computer scientist and roboticist at the University of Vermont.

Bongard was not involved in the neurobot study but is a frequent collaborator of Levin’s. Together, they cofounded the nonprofit Institute for Computationally Designed Organisms and a commercial startup, Fauna Systems, to advance biobot-related technologies.

According to Fauna CEO Naimish Patel, the company is initially targeting environmental sensing applications, aiming to deploy xenobots in settings such as aquaculture, wastewater monitoring, and pollutant detection, where the technology’s ability to integrate multiple signals could provide an early readout of ecosystem health.

If the xenobots encounter a mixture of stressors—say, elevated heavy metals, shifts in pH, and traces of agricultural runoff—their collective changes in movement or activity could provide a sensitive, real-time signal that something in the environment is amiss.

Precedent for this idea comes from Poland, where many cities already use freshwater mussels as living sentinels of water quality, wired with sensors that register when the animals clamp their shells shut in response to pollutants. Xenobots could extend this concept further, Patel says, potentially offering greater sensitivity and specificity by integrating multiple environmental cues into a single, measurable behavioral response. And neurobots could eventually push this fusion of sensing and computation into ever more sophisticated territory, he adds.

But the technical hurdles remain substantial—and the practical opportunities with simpler, non-neural versions are already compelling—so the first-gen xenobots, for the time being, remain the focus of Fauna’s initial product-development efforts, Patel says. “Right now, we’re looking for the intersection between unmet commercial need and emerging capability.”

It’s easy to assume that Robert Woo was defined by the accident that took away his ability to walk.

Certainly, the day of his accident—14 December 2007—was a turning point. Woo, an architect working on the new Goldman Sachs headquarters in New York City, hadn’t attended his company’s holiday party the night before, and that morning he was the only one in the trailer that served as the construction-site office. He was bent over his laptop when, 30 floors above, a crane’s nylon sling gave way, sending about 6 tonnes of steel plummeting toward the trailer. The roof collapsed, folding Woo in half and smashing his face into his laptop, which smashed through his desk.

“I was conscious throughout the whole ordeal,” Woo remembers. “It was an out-of-body experience. I could hear myself screaming in pain. I could hear the voices of the rescue workers. I heard one firefighter say, ‘Don’t worry, we’re getting to you.’” The rescue workers hauled him out of the rubble and got him to the emergency room in 18 minutes flat; with one lung crushed and the other punctured, he wouldn’t have lasted much longer. In those frantic early moments, a doctor told him that he might be paralyzed from the neck down for the rest of his life. He remembers asking the doctors to let him die.

This article is part of the special report “Cyborg Tech From the Inside” →

Woo simply couldn’t imagine how a paralyzed version of himself could continue living his life. Then 39 years old, he worked long hours and jetted around the world to supervise the construction of skyscrapers. More important, he had two young boys, ages 6 months and 2 years. “I couldn’t see having a life while being paralyzed from the neck down, not being able to teach my boys how to play ball,” he recalls. “What kind of life would that be?”

But in a Manhattan showroom last May, Woo showed that he’s not defined by that accident, which left him paralyzed from the chest down, but with the use of his arms. Instead, he has defined himself by how he has responded to his injury, and the new life he built after it.

Robert Woo walks inside the Wandercraft facility in New York City using the company’s latest self-balancing exoskeleton. Nicole Millman

In the showroom, Woo transferred himself from his wheelchair to a 80-kilogram (176-pound) exoskeleton suit. After strapping himself in, he manipulated a joystick in his left hand to rise from a chair and then proceeded to walk across the room on robotic legs. Woo’s steps were short but smooth, and he clanked as he walked.

This exoskeleton, from the French company Wandercraft, is one of the first to let the user walk without arm braces or crutches, which most other models require to stabilize the user’s upper body. The battery-powered exoskeleton took care of both propulsion and balance; Woo just had to steer. The bulky apparatus had a backplate that extended above Woo’s head, a large padded collar, armrests, motorized legs, and footplates. Walking across the room, he appeared to be half man, half machine. On the other side of the showroom’s plate-glass window, on Park Avenue, a kid walking by with his family came to a dead halt on the sidewalk, staring with awe at the cyborg inside.

Robert Woo prepares to walk in a Wandercraft exoskeleton; the device’s controller enables him to stand up, initiate walk mode, and choose a direction. Bryan Anselm/Redux

The amazement on the boy’s face was reminiscent of Woo’s young sons’ reaction when they saw a photo of Woo trying out an early exoskeleton, back in 2011. “Their first comment was, ‘Oh, Daddy’s in an Iron Man suit,’” he remembers. Then they asked, “When are you going to start flying?” To which Woo replied, “Well, I’ve got to learn how to walk first.”

The title of exoskeleton superhero suits Woo. He’s as soft-spoken and mild-mannered as Clark Kent, with a smile that lights up his face. Yet the strength underneath is undeniable; he has built a new life out of sheer determination.

For 15 years, he’s been a test pilot, early adopter, and clinical-study subject for the most prominent exoskeletons under development around the world. He placed the first order for an exoskeleton that was approved for home use, and he learned what it was like to be Iron Man around the house. Throughout it all, he has given the companies detailed feedback drawn from both his architectural design skills and his user experience. He has shaped the technology from inside of it.

Saikat Pal, a researcher at the New Jersey Institute of Technology, in Newark, met Woo during clinical trials for Wandercraft’s first model. Like so many others in the field, Pal quickly recognized that Woo brought a lot to the table. “He’s a super-mega user of exoskeletons: very enthusiastic, very athletic,” Pal says. “He’s the perfect subject.”

By pushing the technology forward, Woo has paved the way for thousands of people with spinal cord injuries as well as other forms of paralysis, who are now benefiting from exoskeletons in rehab clinics and in their homes. “Our bionics program at Mount Sinai started with Robert Woo,” says Angela Riccobono, the director of rehabilitation neuropsychology at Mount Sinai Hospital, in New York City, where Woo became an outpatient after his accident. “We have a plaque that dedicates our bionics program to him.”

Robert Woo walks down a sidewalk in New York City in 2015 using a ReWalk exoskeleton, one of the first exoskeletons designed for use outside the rehab clinic. Eliza Strickland

It’s a fitting tribute. Woo’s post-accident life has been marked by victories, frustrations, deep love, and one devastating loss, and yet he has continued to devote himself to bionics. And while his vision for exoskeletons hasn’t changed, experience has reshaped what he expects from them in his lifetime.

Rebuilding a Life After his Spinal Cord Injury

Long before Woo ever stood up in a robotic suit, he had developed the habits of mind that would later make him an unusually perceptive test pilot.

Woo has always been a builder, a tinkerer, a fixer. Growing up in the suburbs of Toronto, he put together model kits of battleships and airplanes without looking at the instructions. “I just put things together the way I thought it would work out,” he says. He trained as an architect and in 2000 joined the Toronto-based firm Adamson Associates Architects, a job that soon had him traveling to Europe and Asia to work on corporate high-rises.

Adamson specializes in taking the stunning designs of visionary architects and turning them into practical buildings with elevators and bathrooms. “Most of the design architects don’t really have a clue about how to build buildings,” Woo says. He liked solving those problems; he liked reconciling beautiful designs with the stubborn reality of construction. That talent for understanding a structure from the inside and spotting the flaws would prove essential later.

After his accident, Woo had two major surgeries to stabilize his crushed spine, which required surgeons to cut through muscles and nerves that connected to his arms. For two months, he couldn’t feel or move his arms; there was a chance he never would again. Only when sensation began creeping back into his fingertips did he allow himself to imagine a different future. If he wasn’t paralyzed from the neck down, he thought, maybe more of his body could be brought back online. “My focus was to walk again,” he says.

Woo was discharged in March 2008 and went back to his New York City apartment. He was still bedridden and required around-the-clock care. He doesn’t much like to talk about this next part: By May, his then-wife had moved back to Canada and filed for divorce, asking for full custody of their two children. Woo remembers her saying, “I can’t look after three babies, and one of them for life.”

It was a dark time. Riccobono of Mount Sinai, who met Woo shortly after he became an outpatient there in 2008, recalls the despondent look on his face the first time they talked. “I wasn’t sure that he wasn’t going to take his life, to be honest,” she says. “He felt like he had nothing to live for.”

Angela Riccobono of Mount Sinai Hospital [left] credits Woo with jump-starting the hospital’s bionics program; a plaque in the department of rehabilitation medicine recognizes his role. Robert Woo

Yet Woo harbors no animosity toward his ex-wife. “If we hadn’t separated and gone through the custody hearing, I don’t think I would have gotten this far,” he says. To win partial custody of his children, Woo had to become independent. He had to get off narcotic pain medications, regain strength, and learn how to navigate life in a wheelchair. He had to show that he no longer needed constant nursing, and that he could take care of both himself and his boys.

There were milestones: learning how to get back into his wheelchair after a fall, learning to drive a car with hand controls, learning to manage his body as it was, not as it had been. The biggest change came when he reconnected with his high school sweetheart, a vivacious woman named Vivian Springer. She was then dividing her time between Toronto and New York City, and she had a son who was almost the same age as Woo’s two boys. Springer had worked in a nursing home and knew how to change the sheets without getting him out of bed; she was currently working in human resources and knew how to deal with insurance companies. “You wouldn’t believe how much stress it lifted off of me,” Woo says. Over time, they became a family.

Robert Woo’s wife, Vivian, was trained in how to operate the device he used at home. His sons, Tristan [left] and Adrien, grew up watching their dad test exoskeletons. Left: Lifeward; Right: Robert Woo

Once Woo had that foundation in place, Riccobono witnessed a profound change. “He went from focusing on ‘what I can’t do anymore’ to ‘What’s still possible? What can I do with what I have?’” At Mount Sinai, Woo remembers asking his doctor Kristjan Ragnarsson, who was then chairman of the department of rehabilitation medicine, if he would ever walk again. “His response was, ‘Yes, you can walk again,’” Woo remembers, “‘but not the way you used to walk.’”

First Steps in an Exoskeleton

As soon as he had regained use of his hands, Woo had started googling, looking for anything that could get him back on his feet. He tried rehab equipment like the Lokomat, which used a harness suspended above a treadmill to enable users to walk. But at the time, it required three physical therapists: one to move each leg and one to control the machine. It was a far cry from the independent strides he dreamed of.

Several years in, he learned about two companies that had built something radically different: exoskeleton suits for people with spinal cord injuries. These prototypes had motors at the knees and the hips to move the legs, with the user stabilizing their upper body with arm braces. Woo desperately wanted to try one, although the technology was still experimental and far from regulatory approval. So he took the idea to Ragnarsson, asking if Mount Sinai could bring an exoskeleton into its rehab clinic for a test drive. Ragnarsson, who’s now retired, remembers the request well. “He certainly gave us the kick in the behind to get going with the technology,” he says.

Robert Woo tries out an early exoskeleton from Ekso Bionics at Mount Sinai Hospital, where he first began testing the technology. Mario Tama/Getty Images

Ragnarsson had seen decades of failed attempts to get paraplegics upright, including “inflatable garments made of the same material the astronauts used when they went to the moon,” he says. All those devices had proved too tiring for the user; in contrast, the battery-powered exoskeletons promised to do most of the work. And he knew the CEO of Ekso Bionics, a Berkeley, Calif.–based company that had built exoskeletons for the military. In 2011, Ekso brought its new clinical prototype to Mount Sinai.

The day came for Woo’s first walk. “I was excited, and I was also scared, because I hadn’t stood up for almost five years,” he remembers. “Standing up for the first time was like floating, because I couldn’t feel my feet.” In that first Ekso model, Woo didn’t control when he stepped forward; instead, he shifted his weight in preparation, and then a physical therapist used a remote control to trigger the step. Woo walked slowly across the room, using a walker to stabilize his upper body, his steps a symphony of clunks and creaks and whirs. He found it mentally and physically exhausting, but the effort felt like progress.

Robert Woo stands using an exoskeleton and embraces his wife, Vivian. Woo says that exoskeleton use has both physical and psychological benefits. Mt. Sinai

Riccobono was there for those first steps, with tears running down her face. “I remembered how he looked the day I first met him, so defeated,” she says. “To see him rise from the chair, to see him rise to a standing position, to see how tall he was, to see him take those first steps—it was beautiful.” Ragnarsson saw clear benefits to the technology. “Any type of walking is good physiologically,” he says. “And it’s a tremendous boost psychologically to stand up and look someone in the eye.” Woo remembers hugging his partner, Springer, and for the first time not worrying about running over her toes with his wheelchair. I first met Woo a few days later, during his third session with the Ekso at Mount Sinai.

Ann Spungen [left], a researcher at a Veterans Affairs hospital, led early clinical trials of exoskeletons. Her research focused on the medical benefits of exoskeleton use. Robert Woo

Later that same year, at a Department of Veterans Affairs (VA) hospital in the Bronx, Woo got to try a prototype of the world’s other leading exoskeleton: the ReWalk, from the Israeli company of the same name (since renamed Lifeward). VA researchers, led by Ann Spungen, were keen to determine if exoskeleton use had real medical value for veterans with spinal cord injuries. Woo was part of that clinical trial, for which he had more than 70 walking sessions, and he’s since been in many others. But he remembers the first VA trial with the most gratitude. “Dr. Spungen’s first exoskeleton clinical trial really turned things around for me,” he says.

Over the course of the trial’s nine intense months, Woo says he saw noticeable improvements to many facets of his health. “By the end of the trial, I eliminated about three-quarters of my medication intake,” he says, including narcotic pain pills and medication for muscle spasms. He grew fitter, with less body fat, more muscle mass, and lower cholesterol. His circulation improved, he says, causing scrapes and cuts to heal more quickly, and his digestion improved too. The results Woo experienced have generally been borne out in research studies at the VA and elsewhere—exoskeletons aren’t just good for the mind, they’re good for the body.

Improving Exoskeletons From the Inside

During the VA trial, Woo began to think of exoskeletons not as miraculous machines, but as works in progress.

Pierre Asselin [right], a biomedical engineer, worked with Robert Woo during clinical trials of exoskeletons. He says Woo was always pushing the limits of the technology. Robert Woo

Pierre Asselin, the biomedical engineer coordinating the VA’s study, watched participants respond very differently to the equipment. “These devices are not the equivalent of walking—you’re tired after walking a mile,” he says. He notes that later models of both the Ekso and ReWalk enabled users to initiate each step through software that recognized when they shifted their weight. Asselin adds that the cognitive load is “like learning to drive a manual transmission car, where at first you’re really struggling to coordinate the clutch and the brake.” Woo picked it up immediately, he remembers.

Robert Woo uses an exoskeleton to reach items in a kitchen cabinet during a test of the device’s utility for everyday tasks. Eliza Strickland

Woo approached the ReWalk the way he had approached buildings in his previous life: He looked inside the structure and found the weak points. An early model left some users with leg abrasions where the straps rubbed—a small injury for most people, but a serious risk for someone who can’t feel a wound forming. Woo suggested better padding and stronger abdominal supports to redistribute the load. He also hated the heavy backpack that carried the battery and computer, so one afternoon he grabbed an old pack, cut off the straps, and rebuilt it into a compact hip-mounted pouch. Then he snapped photos and sent them to the company. The next model arrived with a fanny pack.

Robert Woo sent detailed design sketches as part of his feedback to exoskeleton engineers. Robert Woo

Sometimes his fixes were more ambitious. One Ekso unit that he used at Mount Sinai kept shutting down after 30 minutes. Woo felt the hip motors and found them hot to the touch. “I said, ‘Can I remove these? I’m going to make a really quick fix, okay? Give me a drill and I’ll put a couple of holes in it,” he recalls telling the therapists, proposing to create a DIY heat sink. He wasn’t allowed to modify the prototype, but a year later the company introduced improved cooling around the hip motors. “There is a Robert Woo design on this device,” one therapist told him.

Eythor Bender, who was then the CEO of Ekso, called Woo to thank him for his feedback and invite him to spend a week at Ekso’s headquarters. “There was no lack of engineering power in that building,” says Bender. “But sometimes when you work with engineers, they overlook important things.” Bender says Woo brought both design skills and lived experience to his weeklong residency. “He told the engineers, ‘Guys, this has to be something that people actually like to wear.’”

Ekso Bionics CEO Eythor Bender [left] and Mount Sinai physician Kristjan Ragnarsson [right] were both on hand for Woo’s early trials of the Ekso device. Ragnarsson says he saw physical and psychological benefits of exoskeleton use. Robert Woo

The longer Woo tested, the further ahead he started thinking. With motors only at the hips and knees, every exoskeleton still required crutches. Add powered ankles, he told the Ekso and ReWalk teams, and the suits could balance themselves, freeing the user’s hands. But Woo was ahead of his time. “They said they weren’t going to do that. They weren’t going to change their whole platform,” he remembers. Years later, though, hands-free exoskeletons like those from Wandercraft would emerge built around exactly that principle.

When the Exoskeleton Came Home

By the mid-2010s, Woo had pushed the technology as far as he could in clinics. What he wanted now was to use an exoskeleton at home.

That milestone came after ReWalk’s exoskeleton became the first to win FDA approval for home use in 2014. ReWalk engineers still remember Woo’s help on the final tests for that personal-use model. It was the end of May in 2015, recalls David Hexner, the company’s vice president of research and development. “He said, ‘Guys, this is great. I’m going to buy it.’”

Woo was the first customer to buy an exoskeleton to bring home, paying US $80,000 out of pocket. His insurance wouldn’t cover the cost, but he was able to make the purchase in part because of a legal settlement after his accident. The home-use model came with a requirement that the user have at least one companion who was fully trained in operating the device. In Woo’s case, that meant that Springer learned to suit him up, realign his balance, and help him if he fell.

On delivery day, two SUVs drove up to a hotel down the street from Woo’s condo in the Toronto area. The technicians hauled two huge boxes into a hotel room and assembled his personal exoskeleton. They took Woo’s measurements, made adjustments, checked the software. This latest version could be controlled by either weight shifting or tapping commands on a smartwatch, and Woo had the app ready. He tested out everything in the hotel room, signed off, and then the technicians drove his robot legs to his home.

That was the start of his golden period with the ReWalk—similar to the excitement many people experience with a new piece of exercise equipment. “I used it every day for a few hours, and then I started logging how many steps I’d done,” Woo says. “My last count was probably just slightly over a million steps,” he says, with half of those steps taken in his home unit and half in training programs and clinical trials.

The ReWalk was the first exoskeleton available for use outside the clinic. Robert Woo’s ReWalk arrived in two large boxes. ReWalk engineers assembled it in a hotel room, and Woo tried it out in the hallway before taking it home. Robert Woo

Tristan, Woo’s eldest son, remembers doing laps with his dad in the condo’s underground parking garage while his dad was training for a 5-kilometer race in New York City. Tristan admits that he had previously been embarrassed about his dad, but training for the race shifted something for him. “I was so used to not wanting to tell people that my dad was in a wheelchair, but then I shared his passion for the training,” he says. “When people would come up to us, I’d tell them about it.”

The ReWalk could turn ordinary moments into small engineering projects. On weekends, Woo would take his boys to the golf course behind their condo and bring a baseball. He had rigged two holsters to the sides of the suit so he could stash a crutch and stand on three points (two legs and one arm) while he pitched or caught. Throw, switch crutches, catch. On the day of his accident, he never thought such a scene would be possible. But with the exoskeleton, it became just another design problem to solve. “It’s a little more work. It’s not perfect,” he says. “But in the end, you still get to do what you want to do—which is play ball with your sons.”

Tristan, now a college student, says he didn’t realize at the time how hard his dad worked to make those mundane activities possible. “Reflecting on it now,” he says, “he has shaped almost every element of my life, and he definitely is my hero.”

But even during that golden stretch, the ReWalk had a way of asserting its limits. Every so often it would freeze mid-stride and require a reboot—a small technical hiccup in theory, but a serious problem when there’s a person strapped inside. Once, when he was walking on his own in the parking garage (without his mandated companion), the suit glitched and went into “graceful collapse” mode, lowering him to a seated position on the ground. Woo had to ask security to bring his wheelchair and a dolly.

He had imagined the exoskeleton would be most useful in the kitchen. Woo loves to cook, and he had pictured himself standing at the stove, looking down into pots, and moving easily between counter and sink. The reality, he found out, was more complicated. “It’s actually very time-consuming and troublesome” to cook in an exoskeleton, he says.

Preparing a meal meant first rolling through the kitchen in his wheelchair to gather every ingredient and utensil, then transferring himself into the ReWalk and moving himself into position at the counter, stopping at just the right moment. “That’s when I fell once,” Woo says. “I collided with the counter and then lost my balance and fell backward.” If all went well, he’d lean either on one crutch or the counter to keep his balance while he worked. But if he’d forgotten to grab the vinegar from the cabinet, he’d have to go into walk mode, crutch over to it, and figure out how to carry the bottle back to his workstation.

Sitting unused in Robert Woo’s home, his ReWalk exoskeleton reflects both the promise and the limits of early devices. Robert Woo

Gradually, he stopped trying. The suit, which he’d once worn every day, spent more time sitting idle in the hallway; like so many abandoned treadmills and stationary bikes, it gathered dust. Part of the reason was the exoskeleton’s practical limitations, but part of it was a shocking development: In 2024, Vivian was diagnosed with an aggressive form of breast cancer. She died in November of that year, at the age of 54.

Woo was scheduled to begin a new round of clinical trials for the Wandercraft home-use exoskeleton that month. In the aftermath of Vivian’s death, he postponed his sessions and questioned whether he would ever go back. “At the time, I thought, ‘What’s the point?’” he remembers.

He did go back, though. “He just rolled up, right into my office,” says Mount Sinai’s Riccobono. “He still had Vivian’s box of ashes on his lap. That’s how fresh it was.” Woo brought the box into a meeting of spinal cord injury patients and shared the story of losing the love of his life. And he told them that he heard his wife’s voice in his head every day, telling him to get back to work. Once again, he was figuring out how to move forward with what he had.

How Close Are We to Everyday Exoskeletons?

In the Wandercraft showroom last May, Woo steered toward the door to the street, technicians flanking him like spotters. The slope down to the sidewalk was barely an inch high, but everyone tensed. He shifted his weight and took a step forward. The suit halted automatically. He tried again—step, stop; step, stop—as the suit kept detecting the slight decline and a safety feature kicked in. The Wandercraft isn’t yet rated for slopes of more than 2 percent, and even the gentle pitch of Park Avenue was enough to trigger its safeguards. When he finally reached the sidewalk, Woo broke into a grin. A man in the back seat of a stopped Uber leaned out his window, filming.

During testing of the Wandercraft exoskeleton, straps caused an abrasion on Robert Woo’s leg, which he documented as part of his feedback to the company. Robert Woo

Woo had recently completed seven sessions with the Wandercraft at the VA hospital and had been impressed overall. But at the showroom, he rolled up his pants leg to reveal an abrasion on his shin, the result of a strap that had worn away a patch of skin during a long walking session. He would later send Wandercraft a nine-page assessment with photos and a technology wish list, asking the company to work on things like padding, variable walking speeds, and deeper squats.

Wandercraft’s engineers relish that kind of user feedback, says CEO Matthieu Masselin. Exoskeletons are a far more difficult engineering problem than humanoid robots, he explains. “You basically have two systems of equal importance. You know about the robot—it’s fully quantified and measured. But you don’t know what the person is doing, and how the person is moving within the device.”

Since Woo began testing exoskeletons 15 years ago, both the technology and the market have made strides. ReWalk and Ekso won FDA clearance for clinical use in the 2010s, and both now sell home-use versions. The companies have sold thousands of exoskeletons to rehab clinics and personal users, and they see room for growth; in the United States alone, about 300,000 people live with spinal cord injuries, and millions more have mobility impairments from stroke, multiple sclerosis, or other conditions. The VA began supplying devices to eligible veterans in 2015, and Medicare recently established a system for reimbursement, a move that private insurers are beginning to follow. What was once experimental is slowly becoming established.

Researchers who test the devices say the technology still has significant limits. Pal, of the New Jersey Institute of Technology, mentions battery life, dexterity, and reliability as ongoing challenges. But, he says with a laugh, “Our bodies have evolved over many millions of years—these machines will need a bit more time.” Pal hopes the companies will keep pushing the technological frontier. “My lifetime goal is to see the day when someone like Robert Woo can wake up in the morning, put this device on, and then live an ordinary life.”

For Woo, the real question about the self-balancing Wandercraft was: Could he cook with it? In the VA hospital’s home mockup, he tried it out in the kitchen, stepping sideways to retrieve items from cabinets and squatting to grab something from the fridge’s lower shelf. For the first time in years, he could work at a counter without leaning on crutches. “The self-standing exoskeleton changes everything,” he says. He imagines a user placing a Thanksgiving turkey on a tray attached to the suit and walking it into the dining room.

Back in the showroom, Woo finishes the demo and brings the suit to a seated position before transferring back to his wheelchair. After so many years of testing prototypes, he’s now realistic about the technology’s timeline. A truly all-day exoskeleton—the kind you live in, the kind that replaces a wheelchair—may be a decade or more away. “It may not be for me,” he says. But that’s no longer the point. He’s thinking about young people who are newly injured, who are lying in hospital beds and trying to imagine how their lives can continue. “This will give them hope.”

This article appears in the May 2026 print issue as “The Man in the Machine.”

The optic nerve is like a high-speed fiber-optic cable between your eyes and your brain. But once that cable is cut, whether through trauma or disease, the nerve cannot be repaired and vision cannot be restored.

Some engineers are working to change that.

Shadi Dayeh, a professor of electrical and computer engineering at UC San Diego, has been developing a technology that could electrically stimulate and regenerate the optic nerve. His work is part of a multidisciplinary initiative called VISION (Viability, Imaging, Surgical, Immunomodulation, Ocular preservation, and Neuroregeneration) Strategies for Whole-Eye Transplant. The project aims to make vision-restoring, whole-eye transplantation a reality.

While whole-eye transplantation was first achieved in 2023, the procedure cannot yet restore sight. Dayeh wants to make whole-eye transplantation “not only anatomically viable but also neurophysiologically useful,” he says. If he succeeds, transplant recipients will actually be able to see out of their new eye.

“The optic nerve is the main highway between the eye and the brain. It’s also one of the hardest pathways to repair,” Dayeh says. “So, from an engineering point of view, it’s a major challenge and a major opportunity.”

But before they can reconnect the optic nerve to the brain, Dayeh’s team first has to understand how these two parts of our bodies communicate. Recently, the team completed what Dayeh calls “a foundational step”: mapping how changes in light, color, and frequency affect the visual axis, from the retina to the optic nerve and the brain.

Learning a visual language

The optic nerve is small, but mighty.

An average adult’s optic nerve is only about 4.5 to 5 centimeters long and roughly 0.5 cm wide. But a cross-section of the optic nerve holds over a million axons, the threadlike projections of nerve cells that conduct electrical impulses.

“The optic nerve is very small and delicate,” Dayeh says. “It’s a densely packed cable that carries an enormous information bandwidth—probably the densest bandwidth cable in our nervous system.”

To understand exactly how this delicate cable transmits visual information, Dayeh’s team has developed biocompatible electrode arrays that wrap around the optic nerve and sit on the visual cortex (the part of the brain that processes visual information) in animal and cadaver studies.

The arrays send electrical pulses across the visual pathway, from the optic nerve to the brain, and record the eye’s and brain’s responses to electrical and visual stimulation. This means the team can see how the optic nerve reads certain visual signals—such as changes in light, color, and contrast—how the optic nerve sends these messages to the brain, and how the brain interprets them.

“It’s like a distributed set of sensors in a communication system,” Dayeh says.

As the technology collects high-resolution data, the team maps the optic nerve and visual cortex to understand what Dayeh calls “the language of the visual pathway”—how visual signals get encoded in the optic nerve and represented in the visual cortex. “The idea is not just to record, but to build a code book across the visual pathway.”

The optic nerve isn’t a straight, uniform cylinder. Its diameter varies along its curving structure. That’s why Dayeh’s team developed electrode arrays that are ultrathin and flexible, ensuring stable placement, “like an electronic skin on the surface of the neural tissue,” Dayeh says.

Adding to the difficulty is the very tricky matter of charging optic and brain tissue. “The visual system is not like a muscle that you can electrically shock and then see what happens,” Dayeh says.

To avoid heating the tissue, Dayeh’s system maintains careful control of the density and spatial spread of the electrical charges. “The thermal load is very important for safety,” he says. “Much of our earlier engineering work went into electrode materials and geometries that can inject charge effectively and safely.”

Regenerating the optic nerve

Understanding the visual pathway’s language is one piece of a larger puzzle. Now that they have successfully mapped optic nerve and visual cortex signals, Dayeh’s team is investigating how their technology can help a severed optic nerve regenerate.

To that end, the electrode interface technology very precisely applies and records controlled, localized electrical stimulations to the optic nerve in order to determine where and how much stimulation can spur regeneration.

“The stimulation is not a magic switch,” Dayeh explains. “It’s a precision tool that assists and accelerates the biological processes of regenerating the neural pathway.”

Dayeh’s work contributes to several efforts aimed at restoring sight, which he considers “one of the most ambitious challenges in regenerative medicine and neurotechnology.” While Dayeh’s team measures, maps, and potentially guides the reconnection between the eye and the brain, other approaches include neuroprotection, or preserving the vision cells and circuits before they’re lost, and visual prosthetics and neural byass systems, which restore sight by delivering information directly to the retina, optic nerve, or visual cortex when the natural pathway cannot function.

Dayeh cautions that optic nerve regeneration is a developing field, and much is as yet unknown. Still, research has shown that, when active, cells can survive longer and can better integrate with surrounding tissue. Dayeh’s technology activates cells electrically. “In a simple sense,” he says, “our goal is to activate the cells so they survive longer.”

For now, optic-nerve regeneration technology is being tested in animals to show that a cut optic nerve can grow axons to the brain and restore vision. Dayeh anticipates that in perhaps three years, after rigorous tests and studies on the technology’s efficacy and safety, studies of the novel technology could be conducted for the first time in humans.

One morning in May 2019, a cardiac surgeon stepped into the operating room at Boston Children’s Hospital more prepared than ever before to perform a high-risk procedure to rebuild a child’s heart. The surgeon was experienced, but he had an additional advantage: He had already performed the procedure on this child dozens of times—virtually. He knew exactly what to do before the first cut was made. Even more important, he knew which strategies would provide the best possible outcome for the child whose life was in his hands.

How was this possible? Over the prior weeks, the hospital’s surgical and cardio-engineering teams had come together to build a fully functioning model of the child’s heart and surrounding vascular system from MRI and CT scans. They began by carefully converting the medical imaging into a 3D model, then used physics to bring the 3D heart to life, creating a dynamic digital replica of the patient’s physiology. The mock-up reproduced this particular heart’s unique behavior, including details of blood flow, pressure differentials, and muscle-tissue stresses.

This type of model, known as a virtual twin, can do more than identify medical problems—it can provide detailed diagnostic insights. In Boston, the team used the model to predict how the child’s heart would respond to any cut or stitch, allowing the surgeon to test many strategies to find the best one for this patient’s exact anatomy.

That day, the stakes were high. With the patient’s unique condition—a heart defect in which large holes between the atria and ventricles were causing blood to flow between all four chambers—there was no manual or textbook to fully guide the doctors. The condition strains the lungs, so the doctors planned an open-heart surgery to reroute deoxygenated blood from the lower body directly to the lungs, bypassing the heart. Typically with this kind of surgery, decisions would be made on the fly, under demanding conditions, and with high uncertainty. But in this case, the plan had been tested in advance, and the entire team had rehearsed it before the first incision. The surgery was a complete success.

Such procedures have become routine at the Boston hospital. Since that first patient, nearly 2,000 procedures have been guided by virtual-twin modeling. This is the power of the technology behind the Living Heart Project, which I launched in 2014, five years before that first procedure. The project started as an exploratory initiative to see if modeling the human heart was possible. Now with more than 150 member organizations across 28 countries, the project includes dozens of multidisciplinary teams that regularly use multiscale virtual twins of the heart and other vital organs.

This technology is reshaping how we understand and treat the human body. To reach this transformative moment, we had to solve a fundamental challenge: building a digital heart accurate enough—and trustworthy enough—to guide real clinical decisions.

A father’s concern

Now entering its second decade, the Living Heart Project was born in part from a personal conviction. For many years, I had watched helplessly as my daughter Jesse faced endless diagnostic uncertainty due to a rare congenital heart condition in which the position of the ventricles is reversed, threatening her life as she grew. As an engineer, I understood that the heart was an array of pumping chambers, controlled by an electrical signal and its blood flow carefully regulated by valves. Yet I struggled to grasp the unique structure and behavior of my daughter’s heart well enough to contribute meaningfully to her care. Her specialists knew the bleak forecast children like her faced if left untreated, but because every heart with her condition is anatomically unique, they had little more than their best guesses to guide their decisions about what to do and when to do it. With each specialist, a new guess.

Then my engineering curiosity sparked a question that has guided my career ever since: Why can’t we simulate the human body the way we simulate a car or a plane?

At a visualization center in Boston, VR imagery helps the mother of a young girl with a complex heart defect understand the inner workings of her child’s heart. Dassault Systèmes

I had spent my career developing powerful computational tools to help engineers build digital models of complex mechanical systems, using models that ranged from the interactions of individual atoms to the components of entire vehicles. What most of these models had in common was the use of physics to predict behavior and optimize performance. But in medicine today, those same physics-based approaches rarely inform decision-making. In most clinical settings, treatment decisions still hinge on judgments drawn from static 2D images, statistical guidelines, and retrospective studies.

This was not always the case. Historically, physics was central to medicine. The word “physician” itself traces back to the Latin physica, which translates to “natural science.” Early doctors were, in a sense, applied physicists. They understood the heart as a pump, the lungs as bellows, and the body as a dynamic system. To be a physician meant you were a master of physics as it applied to the human body.

As medicine matured, biology and chemistry grew to dominate the field, and the knowledge of physics got left behind. But for patients like my daughter, that child in Boston, and millions like them, outcomes are governed by mechanics. No pill or ointment—no chemistry-based solution—would help, only physics. While I did not realize it at the time, virtual twins can reunite modern physicians with their roots, using engineering principles, simulation science, and artificial intelligence.

A decade of progress

The LHP concept was simple: Could we combine what hundreds of experts across many specialties knew about the human heart to build a digital twin accurate enough to be trusted, flexible enough to personalize, and predictive enough to guide clinical care?

We invited researchers, clinicians, device and drug companies, and government regulators to share their data, tools, and knowledge toward a common goal that would lift the entire field of medicine. The Living Heart Project launched with a dozen or so institutions on board. Within a year, we had created the first fully functional virtual twin of the human heart.

The Living Heart was not an anatomical rendering, tuned to simply replicate what we observed. It was a first-principles model, coupling the network of fibers in the heart’s electrical system, the biological battery that keeps us alive, with the heart’s mechanical response, the muscle contractions that we know as the heartbeat.

Academic researchers had long explored computational models of the heart, but those projects were typically limited by the technology they had access to. Our version was built on industrial-grade simulation software from Dassault Systèmes, a company best known for modeling tools used in aerospace and automotive engineering, where I was working to develop the engineering simulation division. This platform gave teams the tools to personalize an individual heart model using the patient’s MRI and CT data, blood-pressure readings, and echocardiogram measurements, directly linking scans to simulations.

Surgeons then began using the Living Heart to model procedures. Device makers used it to design and test implants. Pharmaceutical companies used it to evaluate drug effects such as toxicity. Hundreds of publications have emerged from the project, and because they all share the same foundation, the findings can be reproduced, reused, and built upon. With each application, the research community’s understanding of the heart snowballed.

Early on, we also addressed an essential requirement for these innovations to make it to patients: regulatory acceptance. Within the project’s first year, the U.S Food and Drug Administration agreed to join the project as an observer. Over the next several years, methods for using virtual-heart models as scientific evidence began to take shape within regulatory research programs. In 2019, we formalized a second five-year collaboration with the FDA’s Center for Devices and Radiological Health with a specific goal.

That goal was to use the heart model to create a virtual patient population and re-create a pivotal trial of a previously approved device for repairing the heart’s mitral valve. This helped our team learn how to create such a population, and let the FDA experiment with evaluating virtual evidence as a replacement for evidence from flesh-and-blood patients. In August 2024, we published the results, creating the first FDA-led guidelines for in silico clinical trials and establishing a new paradigm for streamlining and reducing risk in the entire clinical-trial process.

In 10 years, we went from a concept that many people doubted could be achieved to regulatory reality. But building the heart was only the beginning. Following the template set by the heart team, we’ve expanded the project to develop virtual twins of other organs, including the lungs, liver, brain, eyes, and gut. Each corresponds to a different medical domain, which has its own community, data types, and clinical use cases. Working independently, these teams are progressing toward a breakthrough in our understanding of the human body: a multiscale, modular twin platform where each organ twin could plug into a unified virtual human.

How a digital twin of the heart is constructed

A cardiac digital twin starts with medical imaging, typically MRI, CT, or both. The slices are reconstructed into the 3D geometry of the heart and connected vessels. The geometry of the whole organ must then be segmented into its constituent parts, so each substructure—atria, ventricles, valves, and so on—can be assigned their unique properties.

At this point, the object is converted to a functional, computational model that can represent how the various cardiac tissues deform under load—the mechanics. The complete digital twin model becomes “living” when we integrate the electrical fiber network that drives mechanical contractions in the muscle tissue.

Each part of the heart, such as the left ventricle [left], is superimposed with a detailed digital mesh to re-create its physiology. These pieces come together to form an anatomically accurate rendering of the whole organ [right].Dassault Systèmes

To simulate circulation, the twin adds computational models of hemodynamics, the physics of blood flow and pressure. The model is constrained by boundary conditions of blood flow, valve behavior, and vascular resistance set to closely match human physiology. This lets the model predict blood flow patterns, pressure differentials, and tissue stresses.

Finally, the model is personalized and calibrated using available patient data, such as how much the volume of the heart chambers changes during the cardiac cycle, pressure measurements, and the timing of electrical pulses. This means the twin reflects not only the patient’s anatomy but how their specific heart functions.

Building bigger cohorts with generative AI

When the FDA in silico clinical trial initiative launched in 2019, the project’s focus shifted from these handcrafted virtual twins of specific patients to cohorts large enough to stand in for entire trial populations. That scale is feasible today only because virtual twins have converged with generative AI. Modeling thousands of patients’ responses to a treatment or projecting years of disease progression is prohibitively slow with conventional digital-twin simulations. Generative AI removes that bottleneck.

AI boosts the capability of virtual twins in two complementary ways. First, machine learning algorithms are unrivaled at integrating the patchwork of imaging, sensor, and clinical records needed to build a high-fidelity twin. The algorithms rapidly search thousands of model permutations, benchmark each against patient data, and converge on the most accurate representation. Workflows that once required months of manual tuning can now be completed in days, making it realistic to spin up population-scale cohorts or to personalize a single twin on the fly in the clinic.

Second, enriching AI models’ training sets with data from validated virtual patients grounds the AI simulations in physics. By contrast, many conventional AI predictions for patient trajectories rely on statistical modeling trained on retrospective datasets. Such models can drift beyond physiological reality, but virtual twins anchor predictions in the laws of hemodynamics, electrophysiology, and tissue mechanics. This added rigor is indispensable for both research and clinical care—especially in areas where real-world data are scarce, whether because a disease is rare or because certain patient populations, such as children, are underrepresented in existing datasets.

Enabling in silico clinical trials

On the research side, the FDA-sponsored In Silico Clinical Trial Project that we completed in 2024 opened a new world for medical innovations. A conventional clinical trial may take a decade, and 90 percent of new drug treatments fail in the process. Virtual twins, combined with AI methods, allow researchers to design and test treatments quickly in a simulated human environment. With a small library of virtual twins, AI models can rapidly create expansive virtual patient cohorts to cover any subset of the general population. As clinical data becomes available, it can be added into the training set to increase reliability and enable better predictions.

The Living Heart Project has expanded beyond the heart, modeling organs throughout the body. The 3D brain reconstruction [top] shows major pathways in the brain’s white matter connecting color-coded regions of the brain. The lung virtual twin [middle] combines the organ’s geometry with a physics-based simulation of air flowing down the trachea and into the bronchi. And the cross section of a patient’s foot [bottom] shows points of strain in the soft tissue when bearing weight. Dassault Systèmes

Virtual twin cohorts can represent a realistic population by building individual “virtual patients” that vary by age, gender, race, weight, disease state, comorbidities, and lifestyle factors. These twins can be used as a rich training set for the AI model, which can expand the cohort from dozens to hundreds of thousands. Next the virtual cohort can be filtered to identify patients likely to respond to a treatment, increasing the chances of a successful trial for the target population.

The trial design can also include a sampling of patient types less likely to respond or with elevated risk factors, thus allowing regulators and clinicians to understand the risks to the broader population without jeopardizing overall trial success. This methodology enhances precision and efficiency in clinical research, providing population-level insights previously available only after many years of real-world evidence.

Of course, though today’s heart digital twins are powerful, they’re not perfect replicas. Their accuracy is bounded by three main factors: what we can measure (for example, image resolution or the uncertainty of how tissue behaves in real life), what we must assume about the physiology, and what we can validate against real outcomes. Many inputs, like scarring, microvascular function, or drug effects are difficult to capture clinically, so models often rely on population data or indirect estimation. That means predictions can be highly reliable for certain questions but remain less certain for others. Additionally, today’s digital twins lack validation for predicting long-term outcomes years in the future, because the technology has been in use for only a few years.

Over time, each of these limitations will steadily shrink. Richer, more standardized data will tighten personalization of the models. AI tools will help automate labor-intensive steps. And the collection of longitudinal data will improve the model’s ability to reliably predict how the body will evolve over time.

How virtual twins will change health care

Throughout modern medicine, new technologies have sharpened our ability to diagnose, providing ever-clearer images, lab data, and analytics that tell physicians what is presently happening inside a patient’s body. Virtual twins shift that paradigm, giving clinicians a predictive tool.

This “Living Lung” virtual-twin simulation shows strain patterns during breathing. Mona Eskandari/UC Riverside

Early demonstrations are already appearing in many areas of medicine, including cardiology, orthopedics, and oncology. Soon, doctors will also be able to collaborate across specialties, using a patient-specific virtual twin as the common ground for discussing potential interactions or side effects they couldn’t predict independently.

Although these applications will take some time to become the standard in clinical care, more changes are on the horizon. Real-time data from wearables, for example, could continuously update a patient’s personalized virtual twin. This approach could empower patients to understand and engage more deeply in their care, as they could see the direct effects of medical and lifestyle changes. In parallel, their doctors could get comprehensive data feeds, using virtual twins to monitor progress.

Imagine a digital companion that shows how your particular heart will react to different amounts of salt intake, stress, or sleep deprivation. Or a visual explanation of how your upcoming surgery will affect your circulation or breathing. Virtual twins could demystify the body for patients, fostering trust and encouraging proactive health decisions.

How are virtual twins being used in medicine?

A new era of healing

With the Living Heart Project, we’re bringing physics back to physicians. Modern physicians won’t need to be physicists, any more than they need to be chemists to use pharmacology. However, to benefit from the new technology, they will need to adapt their approach to care.

This means no longer seeing the body as a collection of discrete organs and considering only symptoms, but instead viewing it as a dynamic system that can be understood, and in most cases, guided toward health. It means no longer guessing what might work but knowing—because the simulation has already shown the result. By better integrating engineering principles into medicine, we can redefine it as a field of precision, rooted in the unchanging laws of nature. The modern physician will be a true physicist of the body and an engineer of health.

This article appears in the June 2026 print issue as “Your Virtual Twin Could One Day Save Your Life.”

Living cells and tissues grown in the lab are vital tools for helping scientists learn about basic biology and test new drugs. Growing miniature organs on a chip from a person’s stem cells could even one day help doctors test personalized treatments.

Now, researchers have developed a lab-on-a-chip that adds a new feature to these systems: low-power grippers that can hold cells or tiny organ models called organoids in place. The CMOS-compatible lab-on-a-chip features shape-memory grippers and chemical sensors for detecting molecules such as neurotransmitters. The microcage array was presented in San Francisco on 18 February at the IEEE International Solid State Circuits Conference.

Researchers working on this multifunctional system hope it will be used to sense and manipulate biological samples of different sizes and potentially help direct the development of stem cells into organoids, which are used to study basic biology and drugs. Growing neural organoids in lab-on-a-chip systems, for instance, can help biologists study brain development and how it’s impacted by chemicals or drugs. Cage-like grippers could be used to hold samples in place, or to bring tissue samples next to each other to encourage their development.

Building bioelectronic systems directly on a chip is attractive because it makes it easy to integrate many different features, including chemical sensing, electrical sensing and stimulation, and physical manipulation. However, manipulating biological samples on CMOS chips can be tricky, says Adam Wang, an electrical engineer at ETH Zurich. Optical and acoustic tweezers, for example, can heat up, while the electric fields used to generate motion in dieletrophoresis can be weakened by high concentrations of ions in the media used to support cells and tissues. These methods also require continuous power inputs. Wang presented the research on behalf of lead student Zhikai Huang, who was unable to attend.

How the Microcages Work

The ETH chip integrates tiny grippers to “cage” biological samples. These grippers are based on so-called shape-memory alloys, layered metal structures that change their shape in response to electric signals, then hold that shape without the need for any additional power.

The ETH chip holds an array of nine sets of microcages, along with control electrodes and electrodes for chemical sensing. At each spot on the array, cages of three different sizes are nested together like rows of concentric flower petals. Their arms are 100, 150, and 280 micrometers long. The smallest might be used to grab single cells while the largest is designed to grapple with whole organoids.

The arms are made of layered platinum and titanium. Each of the three different-sized sets has its own dedicated control electrode. In response to the polarity and magnitude of a signal, the cage arms will either bend and curl upward or flatten back down onto the surface. The electric signal triggers the movement by changing the electrochemical state of the platinum. Once the cages change shape, they stay in place with no additional power, unless they receive an electrical order to open or close again. The array includes electrochemical sensors in the form of electrodes made of gold, platinum, and palladium. Using different electrode materials with different properties enhances the sensitivity of the system, says Wang. And all these materials can operate in electrolytes, including the cell culture media that help sustain biological cells and tissues in the lab.

At the conference, Wang presented the circuit design, and initial tests using the cages to grip onto glass beads and measure concentrations of ferrocyanide, a chemical commonly used to test lab-on-a-chip sensors. Next, they hope to demonstrate that the array can delicately handle biological cells and organoids, and measure biochemicals such as neurotransmitters. Wang says future versions of the CMOS platform could integrate more electrodes for electrical sensing and stimulation of nerve cells.

Scientists don’t know much about how insects spend their time, but it’s well worth finding out. Insects play key roles in food webs and pollinate our crops, and social insects have a lot to teach us about the basics of friendship formation and communication. An ultralightweight radio-frequency tag designed to be worn by a paper wasp may help scientists get a glimpse at some basic behavioral information that’s long been missing: Where do the animals go when they leave the nest?

The tag is just 20 milligrams—about one third the weight of a drop of water. It was presented on 18 February at the IEEE International Solid State Circuits Conference in San Francisco by doctoral student Yi Shen, who works in the lab of University of Michigan electrical engineer David Blaauw. University of Michigan computer scientist Hun-Seok Kim developed localization algorithms to help spot the tag. Their challenge was to make an ultralightweight transmitter that had sufficient range (1.45 kilometers) and accuracy (0.9 meters) to locate these tiny insects.

They’re not the only ones trying to make more accurate, less intrusive trackers for small critters. Cellular Tracking Technologies (CTT) of Cape May, N.J., sells a 60-mg tracker that’s being used to follow the migration patterns of Monarch butterflies. This tracker uses photovoltaics paired with a capacitor and transmits a Bluetooth signal. Anyone can download an app to help track the butterflies. Other versions of the tracker are designed to be worn by nocturnal bats and are fitted with batteries. To track birds that move during the night as well as during the day, CTT makes systems that combine photovoltaics with a rechargeable battery.

What Wasps Want

But even 60 mg would weigh down a wasp. “Every animal that has been tracked is much bigger than a wasp,” says Elizabeth Tibbetts, who studies their behavior and evolution at the University of Michigan. Tibbetts advised Blaauw on their design.

Honeybees and butterflies get a lot of attention, but “people forget to love wasps,” Tibbetts says. Paper wasps are a gardener’s friend. These pollinators eat nectar and prey on caterpillars. And they don’t typically sting humans.

They also have complex social lives and can even recognize each other’s faces. Tibbetts says life is different when you know that one wasp is Diana and the other is Susan, as opposed to a life where “everyone is just another wasp.” Wasps form friendships and partnerships, though some are loners. When they come out of hibernation in the spring, aggregations of about 10 wasps hang out, fight, scope each other out, and decide which others to join up with in cooperative groups. Some decide not to join a group.

Tibbetts says she and other researchers have been able to watch these complex behaviors because wasps usually return to their nests. Wasp researchers identify individuals by putting colored dots on them. “We don’t know anything about what they do when they’re not at their nests,” she says. Sometimes they don’t come back. Did Susan die, start her own nest, or join up with a different nest? With the right kind of tracker, Tibbetts hopes to find out.

Paper wasps weigh about 125 milligrams. They can carry heavy loads, ferrying caterpillars back to their nests. But Blaauw and Shen sought to keep the tag as light as possible, so that the animals can forage freely. They also had to make sure it would not interfere with the wasp’s aerodynamics, so it needed to be small in addition to lightweight.

Brendan Casey

Getting the right combination of light weight, long range, and positional accuracy was key. Jettisoning the battery was the first step. “Batteries don’t scale,” says Blaauw. A miniaturized battery can’t provide enough current to generate a strong radio signal. Capacitors, which store energy by accumulating charges on surfaces, do better at small scales, Blaauw says. “Really small capacitors can store enough charge now to send a radio pulse,” he says. The capacitor used in the wasp tag weighs just 0.86 mg. A tiny photovoltaic array slowly charges up the capacitor until it has enough energy to generate a radio signal.

The need to aggressively miniaturize the entire system created constraints on the circuit design, Shen says. During transmission, the signal can interfere with other parts of the circuit, including the controller and oscillator. So these parts are isolated from the rest of the circuit during transmission. Blaauw says designing the circuit for a specific biological application led them to come up with new design ideas that would not have occurred to them otherwise. “This problem led us to circuit innovations,” says Blaauw.

Michael Lanzone, a behavioral biologist and CEO of CTT, says the wasp tag is impressive. “A tag that weight gives the rest of us something to push for,” he says.

The 9-square-millimeter tag is attached to circuit board for programming.Yi Shen and David Blaauw

Shen says since paper wasps are active only in the warmer months, the team rushed to test their transmitter on one of the pollinators in time to submit their work to ISSCC. In addition to circuit designs, they used CT scans of a wasp to make sure the tag would fit on the insect and would be unlikely to interfere with its aerodynamics. A collaborator in the biology department put on two pairs of gloves to block the creature’s stinger and affixed the tag. The team took the animal outside, and it rapidly flew out of sight while they tracked it for about a kilometer and a half. So far, so good. This summer, they hope to conduct more tests.

Lanzone says he hopes the University of Michigan technology gets funding and further develops the tag to get it in the hands of researchers. “There’s a lot of cool tech that comes out of university labs, but then you don’t hear about it again. I’m excited to see if they can expand it to the next level.”

“I hope this thing works—it’s going to be so fun to use on wasps,” says Tibbetts.

Inside a cavernous hall at the Swiss-French border, the air hums with high voltage and possibility. From his perch on the wraparound observation deck, physicist Walter Wuensch surveys a multimillion-dollar array of accelerating cavities, klystrons, modulators, and pulse compressors—hardware being readied to drive a new generation of linear particle accelerators.

Wuensch has spent decades working with these machines to crack the deepest mysteries of the universe. Now he and his colleagues are aiming at a new target: cancer. Here at CERN (the European Organization for Nuclear Research) and other particle-physics labs, scientists and engineers are applying the tools of fundamental physics to develop a technique called FLASH radiotherapy that offers a radical and counterintuitive vision for treating the disease.

CERN researcher Walter Wuensch says the particle physics lab’s work on FLASH radiotherapy is “generating a lot of excitement.”CERN

Radiation therapy has been a cornerstone of cancer treatment since shortly after Wilhelm Conrad Röntgen discovered X-rays in 1895. Today, more than half of all cancer patients receive it as part of their care, typically in relatively low doses of X-rays delivered over dozens of sessions. Although this approach often kills the tumor, it also wreaks havoc on nearby healthy tissue. Even with modern precision targeting, the potential for collateral damage limits how much radiation doctors can safely deliver.

FLASH radiotherapy flips the conventional approach on its head, delivering a single dose of ultrahigh-power radiation in a burst that typically lasts less than one-tenth of a second. In study after study, this technique causes significantly less injury to normal tissue than conventional radiation does, without compromising its antitumor effect.

At CERN, which I visited last July, the approach is being tested and refined on accelerators that were never intended for medicine. If ongoing experiments here and around the world continue to bear out results, FLASH could transform radiotherapy—delivering stronger treatments, fewer side effects, and broader access to lifesaving care.

“It’s generating a lot of excitement,” says Wuensch, a researcher at CERN’s Linear Electron Accelerator for Research (CLEAR) facility. “We accelerator people are thinking, Oh, wow, here’s an application of our technology that has a societal impact which is more immediate than most high-energy physics.”

The Unlikely Birth of FLASH Therapy

The breakthrough that led to FLASH emerged from a line of experiments that began in the 1990s at Institut Curie in Orsay, near Paris. Researcher Vincent Favaudon was using a low-energy electron accelerator to study radiation chemistry. Targeting the accelerator at mouse lungs, Favaudon expected the radiation to produce scar tissue, or fibrosis. But when he exposed the lungs to ultrafast blasts of radiation, at doses a thousand times as high as what’s used in conventional radiation therapy, the expected fibrosis never appeared.

Puzzled, Favaudon turned to Marie-Catherine Vozenin, a radiation biologist at Curie who specialized in radiation-induced fibrosis. “When I looked at the slides, there was indeed no fibrosis, which was very, very surprising for this type of dose,” recalls Vozenin, who now works at Geneva University Hospitals, in Switzerland.

How to Measure Radiation Doses

Radiation therapy uses a variety of units to refer to the amount of energy received by the patient. Here are the main ones under the International System of Units, or SI.

Gray (Gy): A measure of the absorbed dose—that is, how much radiation energy is absorbed by the body. One gray equals 1 joule of radiation energy per kilogram of matter. FLASH delivers a single dose of 40 Gy or more in a fraction of a second. Conventional radiation therapy, by contrast, may deliver a total dose of 40 to 80 Gy but over the course of several weeks.

Sievert (Sv): A measure of the effective dose—that is, the health effects of the radiation, with different types of ionizing radiation (gamma rays, X-rays, alpha particles, and so on) having different effects. One sievert equals 1 joule per kilogram weighted for the biological effectiveness of the radiation and the tissues exposed.

The pair expanded the experiments to include cancerous tumors. The results upended a long-held trade-off of radiotherapy: the idea that you can’t destroy a tumor without also damaging the host. “This differential effect is really what we want in radiation oncology, not damaging normal tissue but killing the tumors,” Vozenin says.

They repeated the protocol across different types of tissue and tumors. By 2014, they had gathered enough evidence to publish their findings in Science Translational Medicine. Their experiments confirmed that delivering an ultrahigh dose of 10 gray or more in less than a tenth of a second could eradicate tumors in mice while leaving surrounding healthy tissue virtually unharmed. For comparison, a typical chest X-ray delivers about 0.1 milligray, while a session of conventional radiation therapy might deliver a total of about 2 gray per day. (The authors called the effect “FLASH” because of the quick, high doses involved, but it’s not an acronym.)

Many cancer experts were skeptical. The FLASH effect seemed almost too good to be true. “It didn’t get a lot of traction at first,” recalls Billy Loo, a Stanford radiation oncologist specializing in lung cancer. “They described a phenomenon that ran counter to decades of established radiobiology dogma.”

But in the years since then, researchers have observed the effect across a wide range of tumor types and animals—beyond mice to zebra fish, fruit flies, and even a few human subjects, with the same protective effect in the brain, lungs, skin, muscle, heart, and bone.

Why this happens remains a mystery. “We have investigated a lot of hypotheses, and all of them have been wrong,” says Vozenin. Currently, the most plausible theory emerging from her team’s research points to metabolism: Healthy and cancerous cells may process reactive oxygen species—unstable oxygen-containing molecules generated during radiation—in very different ways.

Adapting Accelerators for FLASH

At the time of the first FLASH publication, Loo and his team at Stanford were also focused on dramatically speeding up radiation delivery. But Loo wasn’t chasing a radiobiological breakthrough. He was trying to solve a different problem: motion.

“The tumors that we treat are always moving targets,” he says. “That’s particularly true in the lung, where because of breathing motion, the tumors are constantly moving.”

To bring FLASH therapy out of the lab and into clinical use, researchers like Vozenin and Loo needed machines capable of delivering fast, high doses with pinpoint precision deep inside the body. Most early studies relied on low-energy electron beams like Favaudon’s 4.5-megaelectron-volt Kinetron—sufficient for surface tumors, but unable to reach more than a few centimeters into a human body. Treating deep-seated cancers in the lung, brain, or abdomen would require far higher particle energies.

They also needed an alternative to conventional X-rays. In a clinical linac, X-ray photons are produced by dumping high-energy electrons into a bremsstrahlung target, which is made of a material with a high atomic number, like tungsten or copper. The target slows the electrons, converting their kinetic energy into X-ray photons. It’s an inherently inefficient process that wastes most of the beam power as heat and makes it extremely difficult to reach the ultrahigh dose rates required for FLASH. High-energy electrons, by contrast, can be switched on and off within milliseconds. And because they have a charge and can be steered by magnets, electrons can be precisely guided to reach tumors deep within the body. (Researchers are also investigating protons and carbon ions; see the sidebar, “What’s the Best Particle for FLASH Therapy?”)

Loo turned to the SLAC National Accelerator Laboratory in Menlo Park, Calif., where physicist Sami Gamal-Eldin Tantawi was redefining how electromagnetic waves move through linear accelerators. Tantawi’s findings allowed scientists to precisely control how energy is delivered to particles—paving the way for compact, efficient, and finely tunable machines. It was exactly the kind of technology FLASH therapy would need to target tumors deep inside the body.

Meanwhile, Vozenin and other European researchers turned to CERN, best known for its 27-kilometer Large Hadron Collider (LHC) and the 2012 discovery of the Higgs boson, the “God particle” that gives other particles their mass.

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CERN is also home to a range of smaller linear accelerators—including CLEAR, where Wuensch and his team are adapting high-energy physics tools for medicine.

What’s the Best Particle for FLASH Therapy?

Even as research on FLASH radiotherapy advances, a central question remains: What kind of particle will deliver it best? The main contenders are electrons, protons, and carbon ions. Each has distinct advantages, limitations, and implications for cost, complexity, and clinical reach.

Electrons—long used to treat surface tumors and to generate X-rays—are light, nimble particles, far easier to control than protons or carbon ions. At low energies, they stop quickly in tissue, but new high-energy systems can drive electrons deeper. Now researchers are working on machines that combine multiple high-energy beams at different angles to let doctors sculpt radiation doses that match the tumor’s shape.

That principle underpins Billy Loo’s PHASER (Pluridirectional High-energy Agile Scanning Electron Radiotherapy) system, developed at Stanford and SLAC and licensed to a startup called TibaRay. An array of high-efficiency linacs generates X-ray beams from many directions at once. Their high output overcomes the inefficiency of electron-to-photon conversion to deliver the dose at FLASH speed. Beam convergence at the tumor and electronic shaping conform the dose in three dimensions, producing uniform coverage with relatively simple infrastructure.

Protons have led the way in early clinical trials, largely because existing proton therapy centers can be adapted to deliver FLASH doses. In 2020, the University of Cincinnati Health launched the first human FLASH trial to use proton beams, to treat cancer that had metastasized to bones. “If I want to be pragmatic, the proton beam is ready to go, so let’s move with what we have,” says Geneva University Hospitals’ Marie-Catherine Vozenin.

Protons can penetrate up to 30 centimeters, reaching deep-seated tumors. But the delivery of protons in a continuous beam limits the dose rates. Also, proton systems are far larger and more expensive than, say, X-ray machines, which will likely constrain their availability to specialized centers.

Carbon ions, used in a handful of elite facilities, offer even higher precision and biological effectiveness compared to electrons and protons. Their Bragg peak—a sudden deposition of energy at a specific depth—makes them appealing for deep or complex tumors. But that unmatched precision comes at a steep price, with each facility costing upward of US $300 million. —T.C.

Unlike the LHC, which loops particles around a massive ring to build up energy before smashing them together, linear accelerators like CLEAR send particles along a straight, one-time path. That setup allows for greater precision and compactness, making it ideal for applications like FLASH.

At the heart of the CLEAR facility, Wuensch points out the 200-MeV linear accelerator with its 20-meter beamline. This is “a playground of creativity,” he says, for the physicists and engineers who arrive from all over the world to run experiments.

The process begins when a laser pulse hits a photocathode, releasing a burst of electrons that form the initial beam. These electrons travel through a series of precisely machined copper cavities, where high-frequency microwaves push them forward. The electrons then move through a network of magnets, monitors, and focusing elements that shape and steer them toward the experimental target with submillimeter precision.

Instead of a continuous stream, the electron beam is divided into nanosecond-long bunches—billions of electrons riding the radio-frequency field like surfers. Inside the accelerator’s cavities, the field flips polarity 12 billion times per second, so timing is everything: Only electrons that arrive perfectly in phase with the accelerating wave will gain energy. That process repeats through a chain of cavities, each giving the bunches another push, until the beam reaches its final energy of 200 MeV.

Much of this architecture draws directly from the Compact Linear Collider study, a decades-long CERN project aimed at building a next-generation collider. The proposed CLIC machine would stretch 11 kilometers and collide electrons and positrons at 380 gigaelectron volts. To do that in a linear configuration—without the multiple passes around a ring like the LHC—CERN engineers have had to push for extremely high acceleration gradients to boost the electrons to high energies over relatively short distances—up to 100 megavolts per meter.

Wuensch leads me to a large experimental hall housing prototype structures from the CLIC effort, and points out the microwave devices that now help drive FLASH research. Though the future of CLIC as a collider remains uncertain, its infrastructure is already yielding dividends: smaller, high-gradient accelerators that may one day be as suited for curing cancer as they are for smashing particles.

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The power behind the high gradients comes from CERN’s Xboxes, the X-band RF systems that dominate the experimental hall. Each Xbox houses a klystron, modulator, pulse compressor, and waveguide network to generate and shape the microwave pulses. The pulse compressors store energy in resonant cavities and then release it in a microsecond burst, producing peaks of up to 200 megawatts; if it were continuous, that’s enough to power at least 40,000 homes. The Xboxes let researchers fine-tune the power, timing, and pulse shape.

According to Wuensch, many of the recent accelerator developments were enabled by advances in computer simulation and high-precision three-dimensional machining. These tools allow the team to iterate quickly, designing new accelerator components and improving beam control with each generation.

Still, real-world challenges remain. The power demands are formidable, as are the space requirements; for all the talk of its “compact” design, the original CLIC was meant to span kilometers. Obviously, a hospital needs something that’s actually compact.

“A big challenge of the project,” says Wuensch, “is to transform this kind of technology and these kinds of components into something that you can imagine installing in a hospital, and it will run every day reliably.”

To that end, CERN researchers have teamed up with the Lausanne University Hospital (known by its French acronym, CHUV) and the French medical technology company Theryq to design a hospital facility capable of treating large and deep-seated tumors with the very short time scales needed for FLASH and scaled down to fit in a clinical setting.

Theryq’s Approach to FLASH

Theryq’s research center and factory are located in southern France, near the base of Montagne Sainte-Victoire, a jagged spine of limestone that Paul Cézanne painted dozens of times, capturing its shifting light and form.

“The solution that we are trying to develop here is something which is extremely versatile,” says Ludovic Le Meunier, CEO of the expanding company. “The ultimate goal is to be able to treat any solid tumor anywhere in the body, which is about 90 percent of the cancer these days.”

Theryq’s FLASHDEEP system, under development with CERN and the company’s clinical partners, has a 13.5-meter-long, 140-MeV linear accelerator. That’s strong enough to treat tumors at depths of up to about 20 centimeters in the body. The patient will remain in a supported standing position during the split-second irradiation.THERYQ

Theryq’s push to bring FLASH radiotherapy from the lab to clinic has followed a three-pronged rollout, with each device engineered for a specific depth and clinical use. The first machine, FLASHKNiFE, was unveiled in 2020. Designed for superficial tumors and intraoperative use, the system delivers electron beams at 6 or 9 MeV. A prototype installed that same year at CHUV is conducting a phase-two trial for patients with localized skin cancer.

More recently, Theryq launched FLASHLAB, a compact, 7-MeV platform for radiobiology research.

The company’s most ambitious system, FLASHDEEP, is still under development. The 13.5-meter-long electron source will deliver very high-energy electrons of as much as 140 MeV up to 20 centimeters inside the body in less than 100 milliseconds. An integrated CT scanner, built into a patient-positioning system developed by Leo Cancer Care, captures images that stream directly into the treatment-planning software, enabling precise calculation of the radiation dose. “Before we actually trigger the beam or the treatment, we make stereo images to verify at the very last second that the tumor is exactly where it should be,” says Theryq technical manager Philippe Liger.

FLASH Therapy Moves to Animal Tests

While CERN’s CLEAR accelerator has been instrumental in characterizing FLASH parameters, researchers seeking to study FLASH in living organisms must look elsewhere: CERN doesn’t allow animal experiments on-site. That’s one reason why a growing number of scientists are turning to PITZ, the Photo Injector Test Facility in Zeuthen, a leafy lakeside suburb of Berlin.

PITZ is part of Germany’s national accelerator lab and is responsible for developing the electron source for the European X-ray Free-Electron Laser. Now PITZ is emerging as a hub for FLASH research, with an unusually tunable accelerator and a dedicated biomedical lab to ensure controlled conditions for preclinical studies.

At Germany’s Photo Injector Test Facility in Zeuthen (PITZ), the electron-beam accelerator [top] is used to irradiate biological targets in early-stage animal tests of FLASH radiotherapy [bottom].Top: Frieder Mueller; Bottom: MWFK

“The biggest advantage of our facility is that we can do a very stepwise, very defined and systematic study of dose rates,” says Anna Grebinyk, a biochemist who heads the new biomedical lab, “and systematically optimize the FLASH effect to see where it gets the best properties.”

The experiments begin with zebra-fish embryos, prized for early-stage studies because they’re transparent and develop rapidly. After the embryos, researchers test the most promising parameters in mice. To do that, the PITZ team uses a small-animal radiation research platform, complete with CT imaging and a robotic positioning system adapted from CERN’s CLEAR facility.

What sets PITZ apart is the flexibility of its beamline. The 30-meter accelerator system steers electrons with micrometer precision, producing electron bunches with exceptional brightness and emittance—a metric of beam quality. “We can dial in any distribution of bunches we want,” says Frank Stephan, group leader at PITZ. “That gives us tremendous control over time structure.”

Timing matters. At PITZ, the laser-struck photocathode generates electron bunches that are accelerated immediately, at up to 60 million volts per meter. A fast electromagnetic kicker system acts as a high-speed gatekeeper, selectively deflecting individual electron bunches from a high-repetition beam and steering them according to researchers’ needs. This precise, bunch-by-bunch control is essential for fine-tuning beam properties for FLASH experiments and other radiation therapy studies.

“The idea is to make the complete treatment within one millisecond,” says Stephan. “But of course, you have to [trust] that within this millisecond, everything works fine. There is not a chance to stop [during] this millisecond. It has to work.”

Regulating the dose remains one of the biggest technical hurdles in FLASH. The ionization chambers used in standard radiotherapy can’t respond accurately when dose rates spike hundreds of times higher in a matter of microseconds. So researchers are developing new detector systems to precisely measure these bursts and keep pace with the extreme speed of FLASH delivery.

FLASH as a Research Tool

Beyond its therapeutic potential, FLASH may also open new windows to illuminate cancer biology. “What is really, really superinteresting, in my opinion,” says Vozenin, “is that we can use FLASH as a tool to understand the difference between normal tissue and tumors. There must be something we’re not aware of that really distinguishes the two—and FLASH can help us find it.” Identifying those differences, she says, could lead to entirely new interventions, not just with radiation, but also with drugs.

Vozenin’s team is currently testing a hypothesis involving long-lived proteins present in healthy tissue but absent in tumors. If those proteins prove to be key, she says, “we’re going to find a way to manipulate them—and perhaps reverse the phenomenon, even [turn] a tumor back into a normal tissue.”

Proponents of FLASH believe it could help close the cancer care gap worldwide; in low-income countries, only about 10 percent of patients have access to radiotherapy, and in middle-income countries, only about 60 percent of patients do, according to the International Atomic Energy Agency. Because FLASH treatment can often be delivered in a single brief session, it could spare patients from traveling long distances for weeks of treatment and allow clinics to treat many more people.

High-income countries stand to benefit as well. Fewer sessions mean lower costs, less strain on radiotherapy facilities, and fewer side effects and disruptions for patients.

The big question now is, How long will it take? Researchers I spoke with estimate that FLASH could become a routine clinical option in about 10 years—after the completion of remaining preclinical studies and multiphase human trials, and as machines become more compact, affordable, and efficient. Much of the momentum comes from a growing field of startups competing to build devices, but the broader scientific community remains remarkably open and collaborative.

“Everyone has a relative who knows about cancer because of their own experience,” says Stephan. “My mother died of it. In the end, we want to do something good for mankind. That’s why people work together.”

This article appears in the March 2026 print issue.

Lab-grown cell therapies for diabetes are edging toward the clinic. Researchers can now coax stem cells to behave like pancreatic islets, the tiny clusters of cells that regulate blood sugar. But even the most promising candidate therapies still need months inside a patient’s body to fully mature and work reliably—and some never quite get there.

Now researchers have found a way to watch—and even gently steer—that maturation in the lab.

A team led by Harvard bioengineer Jia Liu and University of Pennsylvania stem-cell biologist Juan Alvarez embedded soft, stretchable electronics into the tiny clusters to create “cyborg” islet organoids. Woven through with a flexible web of microelectrodes, the miniature pancreas-like tissue can eavesdrop on the electrical chatter of individual lab-grown cells for months as they mature, learning to sense glucose and release hormones in tightly coordinated bursts.

That electrical activity is essential. Islets are part of the body’s neuroendocrine system: Like neurons in the brain, their cells fire voltage-driven signals—and those electrical spikes trigger the release of insulin and glucagon, the twin hormones that stabilize blood sugar levels.

The cyborg islets helped the researchers tease apart how the two main cell types that make up islets—insulin-producing beta cells and glucagon-secreting alpha cells—pass through distinct electrical maturation stages before settling into the synchronized firing patterns seen in mature tissue. Reporting 19 February in Science, the researchers also showed how exposures to rhythmic daily glucose cycles and brief pulses of electrical stimulation sharpened the glucose responsiveness of the cells, suggesting that the road to islet maturity can be engineered, not merely observed.

“It is a testimony of the magic that can happen when two very different fields—beta-cell biology and nano-electronics—collide,” says Torsten Meissner, a stem-cell-therapy researcher at Beth Israel Deaconess Medical Center who was not involved in the research.

Silicon Meets Stem Cells

Integrating flexible bioelectronics directly into lab-made islets opens the door to several practical applications, says Alvarez. For one, the approach could accelerate efforts to refine stem-cell-differentiation recipes, so that lab-grown islets are closer to maturity and “can hit the ground running when transplanted,” he explains.

The embedded electronics could also provide a built-in way to monitor the performance of implanted cell therapies—or even one day form the basis of a true “bionic” pancreas system that automatically stimulates cells to sharpen their insulin response when blood sugar levels begins to rise.

“This is really the future,” Liu says. “I think flexible, stretchable, soft electronics integrated with organoids should become the gold standard for next-generation cell therapies, because you don’t want to transplant large numbers of cells if you have no way to monitor or control what they’re doing.”

Liu has been moving toward this vision for more than a decade, beginning with early work he did as a graduate student on syringe-injectable mesh electronics designed to blend into living brain tissue for long-term neural recording. Instead of rigid probes that scar the brain, the porous, ultraflexible meshes were built to match the softness of cells and move with them.

Together with his Harvard colleagues, Liu first applied the concept to organoids in 2019. The researchers showed that by weaving the stretchable electronics into flat sheets of stem cells as they folded themselves into three-dimensional mini-organs, the devices could become an integral part of the tissue itself. Early demonstrations focused on cardiac tissue, tracking the coordinated electrical waves that drive beating heart cells. Subsequent work pushed the platform into brain organoids and even developing embryos.

How Stem Cells Could Cure Diabetes

Now, with pancreatic islets, Liu is bringing the technology to one of regenerative medicine’s most urgent challenges: building replacement cells for people with type 1 diabetes who, because of an immune system that turns on itself, have lost the cells necessary to keep blood sugar in balance.

Important hurdles remain. Most islet-cell therapies still require lifelong treatment with immune-suppressing drugs, restricting transplants to only the most severe cases in which patients can no longer manage their diabetes with insulin alone. Companies are pursuing two main workarounds: encasing cells in protective capsules or genetically engineering them to evade immune attack. But encapsulation efforts have been plagued by device failures, and gene-edited “stealth” cells remain in the early stages of development.

That is not to say the field has lacked breakthroughs.

Last year, Vertex Pharmaceuticals announced that a full dose of its stem-cell-derived islet therapy, named Zimislecel, had helped people with severe type 1 diabetes produce their own insulin again, enabling 10 of 12 study participants to stop taking insulin injections altogether. The results were hailed as a watershed moment: evidence that lab-grown cells can work inside the human body and a glimpse of a future in which a virtually limitless supply of replacement islets could free the field from its reliance on scarce donor pancreases.

But while the transplanted cells eventually performed as well as fully mature islets taken from deceased donors, it took months inside patients’ bodies for them to reach that level of function—and even then, they didn’t work for everybody.

Passing light through the cyborg organoid while it’s under a microscope shows the flexible electronic device. Qiang Li et al.

Toward Smarter Cell Therapies

That is where the cyborg islets from Liu and Alvarez could make a difference. They won’t address the immune suppression challenge. But they could sharpen the cell product itself.

First, by providing a continuous, single-cell readout of electrical activity, the devices could help companies like Vertex fine-tune differentiation protocols in the manufacturing process—testing growth factors, for instance, or electrical stimulation patterns, and quickly identifying which combinations produce the most mature cells.

“Neuroendocrine connections are missing from current stem-cell differentiation protocols,” notes Omid Veiseh, a bioengineer at Rice University who studies diabetes cell therapies but was not involved in the research. Incorporating cues like those delivered by the embedded electronics “could further enhance differentiation trajectories,” he says. “It’s really innovative.”

Second, the bioelectronic scaffolds could one day act as built-in health monitors, providing real-time feedback on islet performance so clinicians can adjust treatment if function begins to falter—a strategy also being explored by companies such as Minutia, though using different device-based approaches.

And third, by coupling sensing with stimulation, the system points toward a closed-loop future: engineered islets equipped with AI-driven sensors that detect rising glucose and automatically boost insulin output through targeted electrical pulses that nudge the cells back on track.

“I see a lot of value here,” says Jeffrey Millman, a bioengineer at Washington University who helped develop the protocol used to create Vertex’s stem-cell-derived therapy and continues to work on improving the maturation and function of lab-grown islets.

But with major engineering, safety, and regulatory questions still to be resolved, don’t expect cyborg islets to enter clinical trials anytime soon, he cautions. In Millman’s view, the near-term payoff is far more practical: using the system to fine-tune differentiation in the lab to produce islets that secrete insulin more powerfully, respond faster to glucose swings, and require fewer cells to achieve the same therapeutic effect.

It may not be as flashy as a high-tech, closed-loop implant, Millman notes, but getting the cells right from the start should yield better therapies in the end.

When Xiangyi Cheng published her first journal paper as a principal investigator in IEEE Access in 2024, it marked more than a professional milestone. For Cheng, an IEEE member and an assistant professor of mechanical engineering at Loyola Marymount University, in Los Angeles, it was the latest waypoint in a career shaped by curiosity, persistence, and a belief that technology should serve people—not the other way around.

The paper’s title was “Mobile Devices or Head-Mounted Displays: A Comparative Review and Analysis of Augmented Reality in Healthcare.”

XIANGYI CHENG

Employer

Loyola Marymount University, in Los Angeles

Title

Assistant professor of mechanical engineering

Member grade

Member

Alma maters

China University of Mining and Technology; Texas A&M University

Cheng’s work spans robotics, intelligent systems, human-machine interaction and artificial intelligence. It has applications in patient-specific surgical planning, an approach whereby treatment is customized to the anatomy and clinical needs of each individual.

Her research also covers wearables for rehabilitation and augmented-reality-enhanced engineering education.

The throughline of her career is sound judgment based on critical thinking. She urges her students to avoid the temptation to accept the answers they’re given by AI without cross-checking them against their own foundational understanding of the subject matter.

“AI can give you ideas,” Cheng says, “but it should never lead your thinking.”

That principle—honed through uncertainty, disciplinary shifts, and hard-earned confidence—has made Cheng an emerging voice in applied intelligent systems and a thoughtful educator preparing students for an AI-saturated world.

From Xi’an to Beijing: A mind drawn to mathematics

Cheng, born in Xi’an, China, grew up in a household shaped by her parents’ disparate careers. Her father was a mining engineer, and her mother taught Chinese and literature at a high school.

“That contrast between logical and literary thinking helped me understand myself early,” Cheng says. “I liked math, and STEM felt natural to me.”

Several teachers reinforced her inclination, she says, particularly a math teacher whose calm, fair approach emphasized reasoning over punishments such as detention for misbehavior or failure to complete assignments.

“It wasn’t about being right,” Cheng says. “It was about thinking clearly.”

In 2011 she enrolled at the China University of Mining and Technology (Beijing) , where she studied mechanical engineering. After graduating with a bachelor’s degree in 2015, she was unsure where the field would take her.

An IEEE paper changed her trajectory

Later in 2015, she traveled to the United States to study at Case Western Reserve University, in Cleveland.

She initially viewed the move as exploratory rather than a long-term commitment.

“I wasn’t thinking about a Ph.D.,” she says. “I wasn’t even sure research was for me.”

That uncertainty shifted in 2017, when Cheng submitted her “IntuBot: Design and Prototyping of a Robotic Intubation Device” paper to the IEEE International Conference on Robotics and Automation (ICRA)—which was accepted.

“AI can give you more possibilities, but thinking is still our responsibility.”

Intubation is a procedure in which an endotracheal tube is inserted into a patient’s airway—usually through the mouth—to help them breathe. Because placing the tube correctly is not simple and usually must be done quickly, it requires training. That’s why research into robotic or assisted intubation systems focuses on improving speed, accuracy, and safety.

She presented her findings at ICRA in 2018, giving her early exposure to a global research community.

“That acceptance gave me confidence,” she recalls. “It showed me I could contribute to the field.”

Her advisor at Case Western encouraged her to switch from the mechanical engineering master’s program to the Ph.D. track. When the advisor moved to Texas A&M University, in College Station, in 2019, Cheng decided to transfer. She completed her Ph.D. in mechanical engineering at Texas A&M in 2022.

Although she didn’t earn a degree from Case Western, she credits her experience there with clarifying her professional direction.

Shortly after graduating with her Ph.D., Cheng was hired as an assistant professor of mechanical engineering at Ohio Northern University, in Ada. She left in 2024 to become an assistant professor at Loyola Marymount.

Engineering for the body—and the classroom

Cheng’s research focuses on human-centered engineering, particularly in health care. One of her major projects addresses syndactyly, a congenital condition in which a newborn’s fingers are fused at birth. Surgeons rely on their experience to estimate the size and shape of skin grafts to be taken from another part of the body for the corrective surgery.

She is developing technology to scan the patient’s hand, extract anatomical landmarks, and use finite element analysis—a computer-based method for predicting how a physical object will behave under real-world conditions—to determine the optimal graft size and shape.

Xiangyi Cheng designs human-centered intelligent systems with applications in health care and education.Xiangyi Cheng

“Everyone’s hand is different,” Cheng says. “So the surgery should be personalized.”

Another project centers on developing smart gloves to assist with hand rehabilitation, pairing the unaffected hand with the injured one so the person’s natural motion can help guide therapy.

She also is exploring augmented reality in engineering education, using immersive visualization and AI tools to help students grasp three-dimensional concepts that are difficult to convey through traditional learning tools. Such visualization lets students see and interact with a digital world as if they’re inside it instead of viewing it on a flat screen.

Teaching balance in an AI-driven world

Despite working at the forefront of AI-enabled systems, Cheng cautions her students to be judicious in their use of the technology so that they don’t rely on it too heavily.

“AI is not always right and perfect,” she says. “You still need to be able to judge whether the answers it provides are correct.”

As AI continues to reshape engineering, Cheng remains grounded in a simple principle, she says: “We should use these tools. But we should never let them replace our judgment. AI can give you more possibilities, but thinking is still our responsibility.”

In her lab and classroom, Cheng prioritizes independent thinking, critical evaluation, and persistence. Many of her research students are undergraduates, and she encourages them to take ownership of their work—planning ahead, testing ideas, and learning from failure.

“The students who succeed don’t give up easily,” she says.

What she finds most rewarding, she says, is watching students mature. Reserved first-year students often become confident seniors who can present complex work and manage demanding projects.

“Getting to witness that transformation is why I teach,” she says.

For students considering engineering, Cheng offers straightforward advice: “Focus on mathematics. Engineering looks hands-on, but math is the foundation behind everything.”

With practice and persistence, she says, students can succeed and find meaning in the field.

Why IEEE continues to matter

Cheng joined IEEE in 2017, the year she submitted her first paper to ICRA. The organization has remained central to her professional development, she says.

She has served as a reviewer for IEEE journals and conferences including Robotics and Automation Letters, Transactions on Medical Robotics and Bionics, Transactions on Robotics, the International Conference on Intelligent Robots and Systems, and ICRA.

IEEE’s interdisciplinary scope aligns naturally with her work, she says, adding that the organization is “one of the few places that truly welcomes research across boundaries.”

More personally, IEEE helped her see a future she had not initially imagined.

“That first conference was a turning point,” she says. “It helped me realize I belonged.”

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Your smartwatch can track a lot of things, but at least for now, it can’t keep an accurate eye on your blood pressure. Last week researchers from University of Texas at Austin showed a way your smartwatch someday could. They were able to discern blood pressure by reflecting radio signals off a person’s wrist, and they plan to integrate the electronics that did it into a smartwatch in a couple of years.

Beside the tried-and-true blood-pressure cuff, researchers in general have found several new ways to monitor blood pressure using pasted-on ultrasound transducers, electrocardiogram sensors, bioimpedance measurements, photoplethysmography, and combinations of these measurements.

“We found that existing methods all face limitations,” Yiming Han, a doctoral candidate in the lab of Yaoyao Jia, told engineers at the IEEE International Solid State Circuits Conference (ISSCC) last week in San Francisco. For example, ultrasound sensing requires long-term contact with the skin. And as cool as electronic tattoos seem, they’re not as convenient or comfortable as a smartwatch. Photoplethysmography, which detects the oxygenation state of blood using light, doesn’t need direct contact, and indeed researchers in Tehran and California recently used it and a heavy dose of machine learning to monitor blood pressure. However, these sensors are thought to be sensitive to a person’s skin tone and were blamed for Black people in the United States getting inadequate treatment during the COVID-19 pandemic.

The University of Texas team sought a noncontact solution that was immune to skin-tone bias and could be integrated into a small device.

Continuous Blood Pressure Monitoring

Blood pressure measurements consist of two readings—systole, the peak pressure when the heart contracts and forces blood into arteries, and diastole, the phase in between heart contractions when pressure drops. During systole, blood vessels expand and stiffen and blood velocity increases. The opposite occurs in diastole.

All these changes alter conductivity, dielectric properties, and other tissue properties, so they should show up in reflected near-field radio waves, Jia’s colleague Deji Akinwande reasoned. Near-field waves are radiation impacting a surface that is less than one wavelength from the radiation’s source.

The researchers were able to test this idea using a common laboratory instrument called a vector network analyzer. Among its abilities, the analyzer can sense RF reflection, and the team was able to quickly correlate the radio response to blood pressure measured using standard medical equipment.

What Akinwande and Jia’s team saw was this: During systole, reflected near-field waves were more strongly out of phase with the transmitted radiation, while in diastole the reflections were weaker and closer to being in phase with the transmission.

You obviously can’t lug around a US $50,000 analyzer just to keep track of your blood pressure, so the team created a wearable system to do the job. It consists of a patch antenna strapped to a person’s wrist. The antenna connects to a device called a circulator—a kind of traffic roundabout for radio signals that steers outgoing signals to the antenna and signals coming in from the antenna to a separate circuit. A custom-designed integrated circuit feeds a 2.4-gigahertz microwave signal into one of the circulator’s on-ramps and receives, amplifies, and digitizes the much weaker reflection coming in from another branch. The whole system consumes just 3.4 milliwatts.

“Our work is the only one to provide no skin contact and no skin-tone bias,” Han said.

The next version of the device will use multiple radio frequencies to increase accuracy, says Jia, “because different people’s tissue conditions are different,” and some might respond better to one or another. Like the 2.4 GHz used in the prototype, these other frequencies will be of the sort already in common use such as 5 GHz (a Wi-Fi frequency) and 915 megahertz (a cellular frequency).

Following those experiments, Jia’s team will turn to building the device into a smartwatch form factor and testing them more broadly for possible commercialization.