Cochlear implants have transformed the lives of hundreds of thousands of people around the world by restoring lost hearing. But externally worn sound processors outside the ear have long been a source of friction, limiting daily activities and exposing users to stigma. A new, fully implanted cochlear implant from Austrian company Med-El now offers the prospect of round-the-clock hearing without any visible sign.

Since the 1980s, cochlear implants have been used to bypass damaged parts of the ear in people with severe-to-profound hearing loss. Conventional devices feature an external unit worn on the back of the ear that houses a microphone, sound processor, and battery. This unit converts incoming sounds into electrical signals that are transmitted wirelessly through the scalp to an electrode array in the cochlea—a spiral shaped structure in the inner ear that converts sound waves into neural signals.

Med-El CEO Ingeborg Hochmair, who developed one of the first multichannel cochlear implants in the 1970s in collaboration with her husband Erwin, says the ambition to create a fully implantable device dates back to the 1970s, but was held back by microphone and battery technology. But after more than 15 years of development, on Tuesday the company launched the TICI Unico, the world’s first commercially available fully implanted cochlear implant (or TICI, Totally Implantable Cochlear Implant).

The device has received European regulatory approval and the first four commercial implantations were carried out on the morning of the announcement, though it’s not yet available in the United States. The company says a clinical trial of the device with 30 patients found it provided “comparable” hearing performance to Med-El’s conventional implants, while allowing users to hear while asleep, swimming, and recharging.

“For the first time, all components of a cochlear implant, including the microphone, are implanted beneath the skin. There are no external components to wear, to remove, or to manage,” Hochmair said in a press conference. “This is the realization of a dream that I’ve had for a very long time. For the first time, invisible hearing with a cochlear implant is possible.”

How the Fully Implanted Cochlear Implant Works

The implant weighs 20 grams and is made up of four interconnected components. A subcutaneous microphone the width of a U.S. nickel, or a 5 euro-cent coin, sits roughly 6 millimeters beneath the skin of the scalp and connects to a postage-stamp-size “stimulator” unit that houses both sound processing hardware and a rechargeable lithium-ion battery. This is connected to a coil that’s used to transfer data in and out of the device and recharge the battery. Another lead passes signals to the electrode array in the cochlea, which is the same as the ones used in the company’s conventional devices.

The unit is recharged using a car-key-size unit called a GoPod, which attaches magnetically to the charging coil through the scalp. The battery, which cannot be replaced, is designed to last for up to 40 hours on a single charge of about 1 to 1.5 hours. The company says performance data suggests it will be able to maintain a minimum of 24 hours of use per charge for at least 15 years.

Bringing the microphone inside the scalp presented by far the greatest engineering challenge, said Gerhard Mark, senior R&D engineer at Med-El and project leader for the TICI Unico. “Sound behaves differently when it travels through the tissue, so reaching the performance required years of research, testing, and continuous refinement,” he said, though he declined to provide details on how the company solved the problem.

How Does the Invisible Cochlear Implant Perform?

So far, clinical results suggest the device can provide comparable performance to a conventional cochlear implant, said Philippe Lefebvre, head of the ENT department at University Hospital of Liège, in Belgium, who led a six-patient feasibility study on the implant published in Communications Medicine last year. Hochmair added that other studies have found the same, though that data is still awaiting peer review.

Lefebvre said he had expected the invisibility of the device to be the biggest selling point for users, but in reality the ability to hear around the clock was the main benefit they cited. This was echoed by Diana Grosser, a volunteer who received a TICI Unico in 2024 as part of the clinical trial. “The most meaningful change is freedom,” she said. “I can sometimes forget that I’m deaf, live more freely with my children, swim or ride my motorcycle without worrying about my processor pressing, slipping, or getting lost. I can even choose to hear at night, which gives me additional safety.”

Fan-Gang Zeng, director of the center for Hearing Research at the University of California, Irvine, said, “I applaud Med-El’s launch of this long-overdue device, which not only represents the first meaningful innovation in cochlear implants in the past 30 years since electroacoustic stimulation, but also accelerates the competition that the market desperately needs.”

Zeng, who is not affiliated with Med-El, argues that industry conservatism has been a greater barrier than technical challenges and hopes that a first-mover will push other companies to release similar products soon.

Seventeen years ago, cell biologist Donald Ingber and his colleagues at Harvard University’s Wyss Institute for Biologically Inspired Engineering submitted a paper to the journal Science describing their model human lung. It was smaller than a USB stick and made of a clear polymer slab containing narrow channels, which were lined with the type of cells that line a lung’s air sacs and blood vessels. When air was pumped through hollow chambers beside the channels, the device rhythmically expanded and contracted—it “breathed.”

This lifelike movement was a dramatic change from previous generations of lung models, which typically used static cultures of lung tissue that were unable to simulate the movements essential to lung function. When exposed to inflammatory proteins and bacteria, Ingber’s artificial lung reacted much as living lungs would. And exposure to silica nanoparticles used to model the effects of ultrafine particulates revealed that movement affected how tissues absorbed them.

Donald Ingber led the team that developed the first human lung-on-a-chip at Harvard University’s Wyss Institute. Sam Ogden

It was a powerful proof-of-principle demonstration of a system that could be used to test drugs and other chemicals, providing a complement and even an alternative to testing in tissue cultures or in animals. Even so, the editors at Science were hesitant. They rejected the paper and suggested that Ingber’s team also run the tests in mice.

It wasn’t an unreasonable request: Harvard’s lung system was new and comparing the results it generated to results from mice would help validate it. Ingber’s team ran the suggested experiments and resubmitted their study a year later, in 2010, at which point it was published. (It has since been cited by nearly 5,400 other papers.) Still, the incident spoke to how animal models have been the default of modern biomedical research.

An early lung-on-a-chip developed at Harvard’s Wyss Institute used microfluidic channels lined with human cells to reproduce key features of lung function. Wyss Institute at Harvard University

A recent story told by Ilka Maschmeyer, a translational toxicology researcher and executive at the German biotech company TissUse, shows how much things have changed. TissUse specializes in building organ-on-a-chip systems—the conversational name for systems like Ingber’s lung—that are used by pharmaceutical companies for research. A few months ago, says Maschmeyer, a pharmaceutical company approached TissUse after being denied permission by the U.S. Food and Drug Administration to run a clinical trial of a new drug. The problem: It had presented animal data, but the FDA wanted data from organs-on-a-chip or some comparable alternative. The standards had come full circle.

A Breathing Lung-on-a-Chip

The moment spoke to a trend, perhaps even the early days of a fundamental shift, away from the use of animals in toxicology and drug development. “It’s rare still,” says Maschmeyer, “but I think it’s going to be more and more frequent.”

A host of these kinds of alternatives to experiments on animals have been developed over the years. Collectively they’re known as NAMs, an acronym that stands, depending on whom you’re talking to, for new approach methodologies, novel alternative methods, or nonanimal methods. Most NAMs have yet to be rigorously tested, but early studies suggest their potential.

As NAMs have become more sophisticated, the question of how they will be implemented has become less about their technical qualities and more about the practical next steps needed to realize their potential. Validating NAMs—standardizing the systems, conducting head-to-head comparisons with animal experiments—is an enormous challenge. Moreover, simply outperforming animal models is necessary but not sufficient. The adoption of NAMs will require changes in policy, training, and culture.

“This transition process is much more complicated than you would think,” says Thomas Hartung, a toxicologist and director of the Center for Alternatives to Animal Testing at Johns Hopkins University. “It is more about change management than it is about the technology.”

The Technologies Replacing Animal Testing

For decades, animal advocates and many scientists have criticized both the morality and usefulness of experimenting on animals. An estimated 92 percent of all drugs that enter U.S. clinical trials fail to reach the market, sometimes for business reasons but often because the drugs prove ineffective or unsafe in ways that were not predicted by animal experiments. Failure rates are even higher in drugs for heart disease, cancer, and diseases of the brain.

These statistics don’t automatically mean that a reliance on animals is to blame. Flawed study designs are a problem too, and also the sheer confounding complexity of disease. But there’s little question that animals have made poor surrogates for many conditions. And just as animal experiments may mistakenly suggest efficacy or fail to predict harm in humans, they might also erroneously suggest that drugs are ineffective or harmful when they could actually work in humans. Some researchers argue that if aspirin or acetaminophen had been discovered after the advent of modern testing requirements, they might have been abandoned.

TissUse’s Humimic systems use microfluidic chips to culture human tissues and model interactions between organs. A researcher images tissues in a chip during an experiment [top], Humimic chips sit in a temperature-controlled unit [center], and a researcher prepares chips for use [bottom].TissUse (3)

Researchers developing NAMs have pushed these systems far beyond old-fashioned tissue cultures. The new technologies include organoids that more closely mimic the structure, composition, and function of human organs. More humanlike still are organ-on-a-chip systems; alongside Ingber’s lung-on-a-chip are brains, hearts, kidneys, and even placentas on a chip. As many as 10 such organs have been linked together, yielding multi-organ systems that promise to recapitulate many aspects of human physiology—not perfectly, but better than a mouse or a monkey would. Supporting these systems are computational simulations of organs and organisms, and also artificial intelligence tools that analyze data generated by other systems and inform future experiments in a high-powered iterative loop.

Yet even as studies piled up and some pharmaceutical companies started using NAMs in-house, the U.S. regulatory system governing drug developing and testing remained an obstacle to their wider use. NAM proponents were overjoyed, then, when in late 2022 the FDA Modernization Act 2.0 passed into law. It explicitly authorized the use of NAMs in the preclinical studies required of new drugs before they could enter human trials. Previous regulations had mandated animal testing; now the door was open to alternatives. It was a landmark moment. “That was something I didn’t expect to see in my life,” says Maschmeyer.

Although immediate in-the-lab impact was limited, the FDA’s decision was a harbinger of things to come. In 2025, the FDA pledged “to make animal studies the exception rather than the norm” for drug safety testing. Then, in September 2026, the agency followed up by issuing a rule that, if it takes effect, will replace references to “animal tests” in its drug-development regulations with the broader term “nonclinical tests.” The change makes explicit that validated alternatives such as human-cell systems, organs-on-chips, and computer models can be used when appropriate.

Also in 2025, the U.S. National Institutes of Health, the world’s largest public biomedical research funder, announced that researchers applying for grants to study animal models would also need to incorporate nonanimal research, such as real-world data or studies of NAMs. Meanwhile, the European Commission and United Kingdom have announced their own plans to phase out animal testing, and the intergovernmental Organisation for Economic Co-operation and Development updated its influential guidelines to allow for expanded use of NAMs.

NAM proponents say these shifts were essential: If regulators won’t accept NAM results, there’s less incentive to adopt them, especially for researchers already working with animals. Maschmeyer says TissUse’s clients increasingly include scientists whose research has been focused on animals. “I see, within the last year, a change,” says Maschmeyer. “It’s more people who are working with animal models who now have to also add in vitro models.” She traces it mainly to the regulatory shift—a trend Ingber calls “game-changing.”

Proving That NAMs Work

It’s not enough for regulators to say that NAMs can or should be used, though. Even more important is the regulatory apparatus dedicated to assessing how they should be used. This begins with their validation: the process by which experimental methodologies and devices are determined to be reliable and trustworthy. A prototype brain-on-a-chip designed to model a rare neurological disease might work fine in the lab that developed it—but to be validated, the system needs to work in the real world.

“You read about all the organ chips that come out of academic labs, which is great—but that’s not going to change their uptake by the FDA, because you have to get the same results anywhere in the world. It has to be a commercial product. It has to be mass-produced and meet very fine performance criteria,” says Ingber. For example, even minute variations in the hydrogels used as tissue scaffolds in organ chips can produce very different growth patterns.

Emulate’s Organ-Chips are connected to the company’s automated culture system, which supplies the chips with nutrients and controls the flow of fluid through them. Emulate

Workflows and procedures need to be uniform, too. One obstacle to wider use of vascularized tumor-on-a-chip platforms in developing cancer therapies, for example, is the different metrics used by different research groups to characterize blood-vessel function and geometry. Experimental guidelines, workflows, checkpoints, metrics, reporting criteria: All need to be standardized in order for researchers to compare their work and collaborate across platforms. Members of Ingber’s lab coach industry researchers on how to use chips developed by Emulate, a company founded by Ingber. But even with instructions, they still need help with the finer points of tending to stem-cell cultures.

When a NAM is ready for commercial use and researchers know how to use it, the most important test—whether it provides clinical benefit—still remains. A rare-disease organ chip might be reliable, but are the biomarkers it measures actually relevant? If so, are the algorithms that extrapolate chip results to the drug’s in-body effects truly predictive?

Microscopic images reveal the human tissues grown inside Emulate’s Organ-Chips. Bacteria, shown in magenta, interact with mucus and airway cells in a LungChip [top]; an IntestineChip develops structures resembling those that absorb nutrients in the small intestine [center]; and tiny hairlike cilia grow on cells in another LungChip, where they help move mucus and trapped particles out of the airway [bottom].Emulate (3)

Such questions have been answered for some NAMs. For example, a liver-on-a-chip system from Emulate correctly flagged about seven out of every eight drugs that had safely passed animal trials but proved toxic to human livers. A similar study was conducted by researchers from Oxford University and Janssen Pharmaceutica (later renamed Johnson & Johnson Innovative Medicine). That team showed that their computational simulations of human heart cells flagged compounds that caused a type of dangerous heart arrhythmia with 89 percent accuracy, compared to animal studies that were 75 percent accurate.

Such studies, however, are complicated and costly. Emulate’s study required 870 chips and the labor equivalent of 16 full-time employees working for 16 weeks—efforts far beyond the reach of the average lab. If the researchers wanted regulatory approval to use their chip to predict large-molecule drugs rather than the small-molecule drugs they tested, they would have needed to run another such study for that particular use. And comparable studies ostensibly need to be conducted for every commercially available NAM and every context in which they would be used—a vast undertaking. “That’s a challenge,” says Kathy Archibald, founder of Safer Medicines Trust, a United Kingdom–based group that considers animals to be poor models of human biology. “It takes too long and costs too much, and small companies can’t afford to do it.”

Ingber thinks that academic scientists need to collaborate more with industry researchers on NAMs, and that governments should fund those projects. He and other NAM proponents also stress the importance of having access to the necessary data: Without information from preclinical animal studies and human clinical trials, comparisons are difficult, but much of that data is now proprietary. Pharmaceutical companies and regulators need to share it, they say, and the FDA has called for an open-access repository of drug toxicity data. Hartung of Johns Hopkins also suggests that new animal experiments be run in parallel with NAMs, producing side-by-side comparisons.

To Christian Maass, a computational biologist at the German biotechnology company ESQlabs, NAMS are overdue for a showdown with animal models. His company makes “digital twin” systems in which data from organ chip systems inform whole-human simulations of drug outcomes and disease progression. “I love what we are doing,” says Maass, speaking not only of his company but of the whole field. But he adds that researchers have not yet provided “the evidence and the proof that we are doing better or as good as the animal models.”

Maass thinks that head-to-head comparisons are essential to good science. After all, if a NAM doesn’t outperform an animal model, or works best as a complement rather than a replacement, that needs to be known. He also believes such studies could convince skeptics. Maass mentions the debut of the iPhone, when people saw for the first time how well a phone could work without buttons. “That was an ‘aha!’ moment,” he says. But for NAMs, “that moment is still lacking.”

Changing Scientific Habits

Even when those head-to-head comparisons are made, though, and regulations are appropriately changed, adoption can be slow. In the mid-1990s, researchers developed and validated the monocyte activation test—an assay that uses human blood cells to predict immune response—to replace the rabbit pyrogen test, which involves injecting a compound into a rabbit’s ear and monitoring the animal’s rectal temperature. But it wasn’t until 2010 that the European Pharmacopeia—the official Europe-wide standards for such testing—accepted the monocyte activation test as a replacement. And rabbits are still widely used for this test worldwide.

Why the slow pace of change? In part because updates to guidance documents referring to animal tests lagged behind, but also because of inertia within the culture and institutions of science. “The formal requirement may disappear, but the informal expectation persists,” says Kathrin Herrmann, a veterinary scientist and colleague of Hartung’s at the Center for Alternatives to Animal Testing. Regulators, grant reviewers, peer reviewers, journal editors—the human infrastructure of science—often still expect to see animal data and are unfamiliar with NAMs.

Herrmann is now overseeing a survey of early-career researchers working with, or trying to make the switch to, NAMs. “We consistently hear concerns that NAM-only proposals are perceived as risky by funders, that there is pressure to ‘add an animal experiment’ for credibility, that access to NAM infrastructure is limited, and that career trajectories become uncertain when departing from established animal models,” says Herrmann.

The vast majority of drugs entering clinical trials in the United States fail to reach FDA approval [failure rates in pink], with particularly high failure rates in some therapeutic areas.

Animal models are embedded in databases, training programs, and the very culture of research. Scientists who use animals may be reluctant to change; their identities as researchers are tied to animals and, more practically, they’ve spent their careers learning the techniques. A toxicologist who has used rats for decades might understandably look askance when asked to take a chance on unfamiliar chunks of polymer and stem cells—especially when human well-being, or millions of dollars, may ride on the choice. Likewise, an academic scientist whose career was built on animal models may not welcome NAMs; a switch may represent the loss of jobs for lab members whose expertise is no longer relevant. “I could see why it’s a hard thing for people to take it up,” says Ingber.

Education and training is vital, say NAM proponents. The NIH and FDA now offer resources for researchers interested in NAMs, as do their counterparts in other countries embracing the technologies. Herrmann helps run webinars where researchers and regulators learn to use and evaluate NAMs; Hartung’s modules on Coursera, the online learning platform, have been taken by about 12,000 students so far. “These trainees will set up their own labs. They will go to industry. They will replace the old guard,” says Joseph Wu, director of Stanford University’s Cardiovascular Institute.

Wu is also a cofounder of Greenstone Biosciences, a company that uses stem-cell-derived human tissues and AI to model disease and predict drug responses. He’s used that position to introduce researchers to NAMs, helping convince the company’s directors to freely share Greenstone’s large library of stem-cell lines with any academic researchers who want to use them. “I really believe that people should understand how this platform works,” says Wu. “At the end of the day, we’re just trying to advance science.”

With enough time—and funding, incentives, training, education, collaboration, and generational turnover—the research culture of drug development and safety testing may shift. Whether NAMs will be used in other areas of science, though, is an open question. Early-stage drug development and regulatory testing account for roughly 30 percent of animals used in experiments; the rest are used in basic biological research. Replacing those animals is less straightforward, but it may be possible: Ingber describes organ-on-a-chip-based insights into inflammatory bowel disease, preterm birth, and treating viral infections that couldn’t have been made in animals. Hartung calls the adoption of NAMs in toxicology a “lighthouse function,” helping guide the way for other types of research.

“Suddenly, all the dams have opened,” he says.

A “lab on a contact lens” can measure the neurotransmitter serotonin in tears, potentially offering a novel wearable method to noninvasively analyze levels of stress, a new study finds.

“Tears could become a practical, noninvasive source of biochemical information that can be measured repeatedly over time,” says Yangzhi Zhu, director of the biomedical device center at the Terasaki Institute for Biomedical Innovation in Los Angeles. “Most biomarker testing today still relies on blood draws or isolated laboratory measurements, which provide only snapshots. A wearable platform based on a contact lens could eventually make it possible to follow biochemical changes more continuously and in everyday settings.”

Stress is linked to the development of many disorders, such as depression and schizophrenia. Currently, doctors often measure stress using questionnaires or diaries, but these are highly subjective, complicating accurate evaluations. In the new study, researchers sought to create a device that measured serotonin for a potentially objective method to gauge stress.

Serotonin, often called a “feel-good” hormone, plays a central role in regulating mood. It’s found mostly in the digestive tract, blood, and nervous system, but small amounts can also be found in tears. As such, the scientists explored whether tears might offer a noninvasive, easily accessible route to analyze serotonin levels. However, because serotonin is only present at very low concentrations, Zhu says, “a sensor needs to be extremely sensitive while still distinguishing serotonin from other molecules in tears.”

Developing a smart contact lens

Zhu and his team fabricated soft, reusable hydrogel lenses encapsulating flexible biocompatible graphene and silver electrodes printed in serpentine patterns designed to tolerate repeated deformation. A compound called ferrocene was bonded onto the graphene to help detect serotonin in a strong, repeatable manner.

The researchers tested these smart contact lenses in lab dishes with commercially available artificial tears that they laced with serotonin. The lenses could detect as little as 72-trillionths of a mole per liter of serotonin, well below the average serotonin concentration in human tears of roughly 15-billionths of a mole per liter. Experiments also showed the lenses could withstand more than 28 days of repeated flipping, folding, stretching, and twisting while staying functional.

The scientists also tested the lenses in lab dishes on tears collected from 10 volunteers five minutes before, immediately after, and roughly 30 minutes after they each performed a pair of stressful tasks—public speaking and math challenges. As expected, serotonin levels in tears fell with increased stress.

In addition, the researchers placed one of their lenses on the eye of an anesthetized live pig for about five minutes. The lens could detect serotonin when the eye was given artificial tears containing 50 and 100 nanomolar levels of the hormone. In addition, the lens caused no sign of infection or irritation.

“We were able to detect very low concentrations of serotonin in tears using a soft contact-lens platform while preserving the transparency, flexibility, and comfort-related properties of the lens,” Zhu says.

When the lenses were used to detect serotonin in the lab and with the pig, the scientists connected the lenses to readout equipment using soft flexible nickel wires. Zhu and his colleagues have developed a proof-of-concept wireless version of their lens incorporating a miniaturized near-field communications (NFC) chip and stretchable antenna for battery-free sensing and smartphone-based data transmission. However, they say further optimization, safety testing, and validation are needed. (Corrective versions of these lenses could also be made, Zhu says.)

Promise for noninvasive biosensing

All in all, “I think the work highlights an exciting direction in wearable biosensing—moving beyond physical signals such as heart rate and temperature toward continuous monitoring of molecular information,” says Wei Gao, a professor of medical engineering at the California Institute of Technology who did not take part in this research. “The eye and tear fluid provide an interesting interface for this because they may enable repeated biochemical measurements without blood sampling.”

In tests where the scientists limited the movements of mice for a few hours per day in order to increase their stress, the researchers found that serotonin levels dropped in both the rodents’ blood and tears to a similar degree. These findings suggest that tears may provide a noninvasive window into blood serotonin levels, but more testing is needed before doctors might use these lenses in medicine, Zhu says.

“We need to understand how tear serotonin varies across different individuals, times of day, stress conditions, ocular surface states, and disease conditions, and how those measurements relate to blood biomarkers and clinical assessments,” Zhu says.

The researchers also measured the stress hormone cortisol in the lab tests of the tears as they used the lenses to measure serotonin. A number of techniques are already being developed to monitor cortisol, such as patches measuring it in sweat or fluid under the skin. Zhu says measuring both serotonin and cortisol can provide complementary information—cortisol measures immediate, short-term responses to stress, while serotonin gives a picture of what a person faces in the long term. He adds that detecting serotonin is also more challenging and shows what they can do with their technology.

In the future, the scientists would like to move beyond measuring only serotonin using their lenses, and to analyzing multiple molecules at the same time for “a much richer picture of a person’s physiological state,” Zhu says. “The long-term goal is to develop a comfortable, wearable platform that can track biochemical changes over time in everyday life.”

The scientists detailed their findings 16 September in the journal Science Translational Medicine.

Typically, if you swallow a battery, it’s a mistake that calls for a trip to the hospital. But that’s not the case with a new ingestible paper battery developed by scientists at MIT. The battery could be used to power medical devices inside the body before safely breaking down.

In tests in live pigs, the paper battery “generated sufficient power to perform meaningful functions inside the gastrointestinal tract, including meter-scale wireless communication and continuous electrical stimulation over several days,” says Giovanni Traverso, a professor of mechanical engineering at MIT.

Smart Pills and Ingestible Electronics

There is a broader trend in healthcare to develop smart pills. These ingestible electronics can analyze and treat the body from within in real time. However, ingestible electronics need a safe and reliable source of power.

Conventional alkaline or lithium-ion batteries can prove deadly if their protective casings leak inside a person. Previous research has sought to develop energy-harvesting techniques for ingestible electronics, but these usually generate low and inconsistent levels of power. Scientists have also designed biodegradable batteries, but these often can only store limited amounts of energy.

Traverso and his colleagues developed a paper-based battery with electrodes made from magnesium and molybdenum trioxide, as detailed in a paper published 21 September in Nature Chemical Engineering. Prior work had explored both materials for use as biodegradable batteries but had used bulky adhesives to bind large particles of these materials into batteries, resulting in thick electrodes that took longer to break down.

The new batteries instead use cellulose nanofibrils (essentially, paper) to bind the electrode materials to the battery. These fibrils are sturdy, can load electrode particles into their porous structures, and can be thin and biodegradable, Traverso says.

Battery electrolytes—which help electric charges shuttle between a battery’s electrodes to discharge electricity—are often toxic. The new paper battery uses a biodegradable ionic liquid gel based on choline chloride and lactic acid as its electrolyte, which yielded more stable performance than several other biodegradable electrolyte approaches, Traverso says.

“Encapsulation was also critical,” Traverso says. “If gastric fluid reached the battery too quickly, its capacity declined prematurely. If the encapsulation persisted for too long, the device would not degrade as intended. Natural wax coatings provided a way to delay fluid penetration and tune the operating lifetime.”

Researchers developed bioresorbable electrodes for an ingestible paper battery. After 90 days under accelerated conditions, the electrode material has almost completely dissolved.Source images: Mehmet Girayhan Say, Ada Erus, et al.

Paper Battery for Gastrointestinal Devices

In tests in live pigs, the batteries generated a peak voltage of 1.84 volts and remained operational in the stomachs of the swine for up to three days. They could power an RFID tag in a pill that, after it was swallowed, maintained stable wireless communication with a receiver 1.5 meters away. Similar smart pills could theoretically help doctors track whether a patient has indeed taken prescribed medication. The batteries could also power a capsule that electrically stimulated the stomach for 20 minutes, an electroceutical technique used to treat gastrointestinal conditions involving impaired digestive function, nausea, or loss of appetite. There was no notable tissue damage from this stimulation. The RFID tag and the circuit board used for electrical stimulation was not biodegradable, but was naturally excreted.

“The battery could potentially power ingestible sensors that measure temperature, pH, pressure, motility, or biochemical signals; systems that transmit physiological information; controlled drug-delivery devices; temporary electrophysiological recording systems; and gastric or intestinal electroceutical devices,” Traverso says. “It may be particularly valuable for devices designed to remain in the stomach for several days, because a bioresorbable power source could reduce or eliminate the need for retrieval.”

The scientists are now prototyping devices that combine these new batteries with biodegradable antennas and RFID systems. The goal is a human clinical trial of a device that can monitor how well patients are taking medicine, “which we aim to start in about two years,” Traverso says.

Prosthetic arms and hands often stop working when wearers start to sweat. A New Zealand team of engineers and assistive technologists argues that a small change—using light-based sensors on the skin instead of electrical sensors that pick up nerve activity—could fix the problem.

For decades, electrical muscle sensors have been the default way for a device to infer the user’s desired hand, wrist, and finger motions. By placing electrodes on a prosthetic-wearer’s arm near key muscle groups, the sensors can read tiny electrical signals from the muscles. However, despite decades of refinement, electrical sensor-controlled prosthetics still face fundamental limitations, including sweat weakening the connection and the prosthetic causing discomfort and requiring frequent readjustment.

For more than a decade, scientists have experimented with infrared and visible-light sensors to detect a prosthetic user’s muscle activity and use that activity to guide the prosthetic device. Last month, researchers at the University of Waikato in Hamilton, New Zealand presented new research on a light-sensor-based prosthetic.

“We’ve got these papers and data out there now that say those who require prosthetics have incredibly high rejection rates of those devices,” says Mahonri Owen, senior lecturer in mechanical engineering at the University of Waikato. He adds they wanted to develop a prosthetic arm that might help “increase the sense of belonging [users] feel and hence improve the likelihood of them accepting the prosthetic.”

Owen and colleagues recently unveiled a device they’ve developed that uses optical and infrared sensing units in a custom harness. The photodiode detectors are mounted close to the skin surface to cover distinct muscle groups.

“There are novel tightening mechanisms on that band as well,” Owen says. “Which also reduces motion artifacts.”

Improving Prosthetic Arm Tech for Better Operation

He adds that multiple armband sizes and configurations could improve user comfort and help stem a persistent clinical problem in upper-limb prosthetics—high device abandonment rates.

“Right now, they’re trying to push this one-size-fits-all thing, and I don’t think that’s great,” he says. “I think we’ve got work to do as engineers.”

According to Owen, the team continues to iterate its designs—including when an African student flagged the light sensor’s reading of darker skin tones. “Skin tone or color of skin can affect [sensor] measurements,” Owen says. “We want to have this technology available to everyone.”

Owen adds that future research could include developing hybrid prosthetics that use both electrical sensors (for accurate signals when there’s a good connection) and optical-based sensors (for better connections against sweat, motion, and armband dislocation from regular use).

Those who need prosthetics, Owen says, can potentially benefit from the assistive technology in ways that are not easily quantified. So the stakes are high, he says, for researchers to design the devices well. “If a mother without arms is then given the opportunity to hold her baby again, you know that we’re not talking about an artificial device anymore,” he says. “We’re talking about a relationship.”

DNA is often explained as a codebook or set of instructions for producing proteins, and ultimately, life. Some stretches of DNA, called genes, code for proteins, but the vast majority of DNA is considered “noncoding.” Some of it has no known function, while other segments are critical to regulating gene activity.

These regulatory elements can interact in complicated ways, and their effects can vary across different cells and tissues. Some also influence genes located far away in the genome. Understanding how changes in DNA affect this regulation “is fundamental to understanding most disease,” says Carl de Boer, a genomicist at the University of British Columbia.

That’s why researchers are working to understand what every imaginable small variation in human DNA across the entire genome might mean for gene regulation. A recent AI tool built for that purpose from Google DeepMind, AlphaGenome, was originally announced in 2025. In January, a paper published in Nature provided more details, and the model was released for public noncommercial use. The AI model can compare an original DNA sequence with an altered one and predict how the change might affect gene expression and other regulatory activity. But researchers had to select the variants they wanted to test, write code, and run the computationally demanding model themselves.

Now DeepMind has done that work in advance for all 9 billion possible single-letter changes to a reference human genome. Today, on 8 September, DeepMind announced the creation and public release of the AlphaGenome Atlas, an online repository of precomputed predictions made using the AlphaGenome model. The Atlas offers a more approachable interface for scientists, without the need to write code or run the AlphaGenome model themselves. It also includes a much-requested new feature, a single-number impact score intended to show at a glance if a variant is likely to be meaningful.

“Understanding our DNA is a grand challenge,” says Pushmeet Kohli, VP of science at Google DeepMind. “Understanding this language of life can unlock so many things.”

The AlphaGenome predictions have some important limitations. For example, many diseases are associated with multiple genetic variants. And although AlphaGenome looks at a relatively large segment of DNA surrounding the variant in question—1 million base pairs—some DNA sequences, called enhancers, can regulate genes over very long distances, sometimes beyond the model’s field of view. Their effects are difficult to predict.

But the Atlas could still help scientists filter possibilities and prioritize lab experiments that would validate its predictions. In that way, it could greatly accelerate work in fundamental biology, disease research, and treatment development, says Žiga Avsec, the genomics lead at DeepMind.

“It seems like they made a useful resource for people,” says de Boer, who recently helped create a framework for better comparisons of computational models similar to AlphaGenome. He is not affiliated with DeepMind.

Although de Boer considers AlphaGenome the “field’s leading model,” he notes that it’s also “very slow and computationally intensive.” The Atlas could benefit people without access to newer hardware, or simply reduce the number of people repeating the same simulations.

The Atlas is freely available for noncommercial research, with the potential for commercial licensing.

Computing 9 Billion Predictions

The entire human genome contains roughly 3 billion base pairs. At each position there are three possible single-nucleotide substitutions, and therefore 9 billion variants in the Atlas. The complete dataset is around 1 petabyte.

“When we started thinking about this project, it seemed impossible to do that computationally,” says Avsec. Early estimates told the team they would need to improve their calculation speed by a factor of 80 in order to compile the Atlas in a reasonable amount of time.

To reach that target, the team gained advantages using a few different techniques, including model distillation, GPU kernel optimization, and the elimination of redundant calculations. “There was a lot of thought and engineering that we had to do in order to make this happen at this scale,” says Avsec.

AlphaGenome and the Atlas build on years of related work at DeepMind. In 2020, AlphaFold predicted the three-dimensional structure of proteins from amino-acid sequences. In 2023, AlphaMissense predicted whether 71 million possible variants that alter proteins were likely benign or pathogenic. Similar to the new Atlas, prediction results from those projects were made available in a public database.

The Atlas allows a scientist to look up a single variant and see more detailed information about the model’s prediction, including 11 different output types. But the top-line figure is a single-number impact score, which by its nature is a simplification of many aspects of those predictions.

“It has a clear use, but it also is probably going to be easily misinterpreted,” says de Boer. “We’re talking about a very complex system, and there’s a lot of moving parts.”

In a display case on the lower level of the Faraday Museum at the Royal Institution in London, there’s an unassuming stack of gray metal discs and blotting paper. It’s not at all obvious that this humble object is the starting point of today’s multibillion-dollar global battery industry. The object’s invention in 1799 grew out of a disagreement that Alessandro Volta—the Italian physicist for whom the unit of measurement for electrical potential is named—had with his friend Luigi Galvani over a dead frog.

The Debate Over Animal Electricity

Galvani was a well-respected Italian physician. In the 1770s, he began investigating the use of electricity to stimulate the muscles of dissected frogs. Armed with an electrostatic generator and an early type of capacitor called a Leyden jar, he was able to create a charge, store it, and then zap his animal specimens at will. He was intrigued when the frog legs twitched as if they were still alive. He spent the last three decades of the 18th century studying the phenomenon, and in 1791, he published De viribus electricitatis in motu musculari commentarius (Commentary on the Effect of Electricity on Muscular Motion).

Luigi Galvani spent decades investigating what he believed to be a natural electric force emanating from animals. Universal History Archive/Getty Images

Galvani saw the frog as embodying an “animal electricity,” an innate vital force that activated nerves and muscles, similar to what had been observed in (living) electric eels and torpedo rays. For Galvani, the frog was an electrical machine analogous to a Leyden jar. The brain was the source of the electrical charge; the nerves conducted the electrical fluid; and the muscles stored opposite charges. The illustrations in his 1791 book are fabulous—frog legs spread all over his laboratory table!

Galvani was wrong in thinking that his frogs were electrical machines, but he was right that the muscle contractions were caused by electric signals.SSPL/Getty Images

At first, Volta, chair of physics at the University of Pavia, concurred with his friend. But after beginning his own experiments, he concluded that Galvani was wrong and that the frog generated no electricity at all. He thought of the frog as nothing more than an electroscope, an instrument to indicate the presence of an electrical charge. Volta posited that the source of the charge Galvani observed came from two different metals in contact with the frog. He termed this “metallic electricity.”

Alessandro Volta came to disagree with Galvani’s theory of animal electricity.Apic/Getty Images

To prove his point, Volta created an “artificial electric organ.” He stacked alternating discs of copper and zinc, separated by cardboard, blotting paper, or cloth soaked in brine or acid. When the top and bottom plates were connected, an electric current flowed through the stack. As opposed to a Leyden jar, which is essentially a capacitor that can store an electric charge and release it in a brief powerful discharge, his stack of discs generated its own electricity through a chemical reaction and delivered a sustained low-current output.

Volta didn’t publicly demonstrate or announce his artificial electric organ until after Galvani died in 1798. But when he finally did, in 1799, it immediately began upending science. Just six weeks after Volta wrote to the Royal Society about his invention, the English scientists William Nicholson and Anthony Carlisle used a voltaic pile to run a current through water to separate it into hydrogen and oxygen. They had discovered chemical electrolysis. Humphry Davy later used a large voltaic pile to isolate a number of elements, including potassium, sodium, calcium, strontium, and barium. Early piles petered out after a few hours. Users who stacked up more metal discs to make more powerful piles found the weight of the discs squeezed out the moisture in the paper or cloth.

Invented in 1799, Volta’s “artificial electric organ” (later known as the voltaic pile) was the first battery. Volta presented this one to Michael Faraday in 1814.Royal Institution of Great Britain/Science Source

One of the most enthusiastic users of the voltaic pile was Galvani’s nephew, Giovanni Aldini, who spent much of his career defending his uncle’s ideas. Aldini created spectacles across Europe in which he used voltaic piles to shock the carcasses of livestock and, occasionally, the bodies of recently executed convicts. Vivid descriptions in the popular press, as well as Aldini’s own writings, raised the question of whether electricity could bring the dead back to life. Mary Shelley provided her answer in her 1818 novel, Frankenstein; or, The Modern Prometheus. In an introduction to an 1831 edition, Shelley cites galvanism as one of her inspirations for the monster’s reanimation process.

Beyond Winners and Losers in Scientific Debates

Scientists and historians share a common trait: They like stories with clear winners and losers. The narrative of competition helps drive a narrative of progress that makes it look like humanity is always moving forward. In the case of Galvani and Volta, Volta is usually depicted as the clear winner in the debate over animal versus metallic electricity. The Encyclopedia Britannica goes as far as to write that “with his announcement of the first electric battery in 1800, victory was assured for Volta.”

But both science and history are more nuanced than that. In fact, Galvani and Volta were both partially right and partially wrong. There was no universal force of animal electricity, but Galvani was correct that electrical signals caused muscle contractions, which he discussed in his anonymous 1794 publication Dell’uso e dell’attività dell’arco conduttore nella contrazione dei muscoli (On the Use and Activity of the Conductive Arch in the Contraction of Muscles). Volta was right to push back on Galvani’s animal electricity theory, but he was wrong that electrophysiological effects require two different types of metal, or any metal at all; the circuit in the voltaic pile was closed by the wet paper or cloth.

It seems a little presumptuous for the Encyclopedia Britannica to declare Volta the winner and Galvani the loser. Volta definitely thought his friend was wrong, but he waited until after Galvani’s death to make his views public. It’s closer to the truth to say they were both genuinely curious to understand the nature of electricity. In the process, they unknowingly helped develop different fields of inquiry: electrophysiology for Galvani and electrochemistry and battery science for Volta.

RELATED: Who Really Invented the Rechargeable Lithium-Ion Battery?

Such an outcome is actually quite common in scientific disagreements. For example, Isaac Newton’s dispute with Christiaan Huygens over the nature of light—did light consist of particles, or corpuscles, as Newton termed them, or waves, as Huygens contested—breaks down today into quantum optics and classical optics. Similarly, Louis Pasteur’s and Justus von Liebig’s debate over fermentation (microorganisms versus chemical decomposition) led to two complementary fields: microbiology and biochemistry.

Maybe instead of looking for winners and losers, we would be better off expanding our horizons and considering the multiple paths of inquiry and discovery. Writing in 1816, toward the end of his career, Volta graciously acknowledged Galvani’s pioneering work, saying “it contains one of the most beautiful and surprising discoveries and the germ of many others.” What new revelations are waiting to develop out of today’s scientific debates?

Part of a continuing series looking at historical artifacts that embrace the boundless potential of technology.

An abridged version of this article appears in the September 2026 print issue as “The First Battery.”

References

On 20 March 1800, a year and three months after the death of Luigi Galvani, Alessandro Volta wrote a letter (in French) to Joseph Banks, president of the Royal Society, describing his invention of an artificial electric organ. It was read before the Society on 26 June and published in Philosophical Transactions on the last day of that year as “On the electricity excited by the mere contact of conducting substances of different kinds.”

The Smithsonian Institution Libraries used their rare books in the online exhibit The Body Electric, which has more information on both Galvani and Aldini.

The website of the Whipple Museum in Cambridge, England, has a number of pages devoted to frogs, including a very informative description of the role frogs played in Galvani’s experiments and how those led to Volta’s work.

For years, brain-computer interfaces (BCIs) have promised to help people with severe paralysis communicate. The technology is still experimental, but more researchers and companies like Neuralink and Synchron are now testing these devices in human clinical trials. Geneva-based Ability Neurotech is the latest to join their ranks, as it gears up to move its optical BCI from brief tests during surgery toward months-long use in a clinical trial.

Last month, the company started an intraoperative study in Germany, recording neural signals from patients undergoing brain tumor surgery in brief, 20 to 30-minute sessions. Later stages of the ongoing study will use the device to record brain activity in up to five conscious patients as they perform speech and movement tasks.

A separate chronic clinical trial in the Netherlands, planned for late 2026, will also test the system over a longer period. As part of a project aiming to develop a BCI to restore speech for paralyzed individuals, University Medical Center Utrecht is recruiting participants for a year-long study in which people with amyotrophic lateral sclerosis (ALS) will receive the implant, train with it at home, and undergo repeated brain-signal recordings.

Sending Brain Data with Light

Ability’s system differs from many other experimental BCIs in a few key ways. For one, the implant uses electrocorticography (ECoG) electrode arrays that rest on the brain’s surface—a distinction from other BCI designs that place penetrating electrodes inside brain tissue. It can monitor neural signals from 128 separate channels at once, sampling each 30,000 times every second to capture a highly detailed recording of brain activity.

Unlike the usual radio frequency transmission method used in wireless electronics, the implant uses an infrared laser-based optical link to stream brain data through the skin at speeds of up to 50 megabits per second. The data is received by an external headpiece and sent to a separate processor for decoding. The wearable also powers the implant wirelessly through induction, eliminating the need for an internal battery that could eventually require replacement.

Ability Neurotech CEO Rotem Kopel says the company evaluated several ways to handle the large data stream before choosing the optical link. The goal was to get raw recordings out of the implant without reducing them first. Ability doesn’t downsample or compress the data before transmission, leaving decoding and processing to equipment outside the body.

That extra information can be valuable in decoding, according to Maitreyee Wairagkar, a project scientist in the Neuroprosthetics Lab at the University of California, Davis, who says Ability’s combination of a battery-free design and optical link “sounds very promising for chronic ECoG recordings.”

“Preserving the information available in raw neural data through transmission is also useful for decoding purposes, since precious data is not lost due to transmission limitations,” Wairagkar says. “I think this is the right focus for longitudinal BCI use, as it offers flexibility over neural feature extraction, which can be useful for improving decoding performance.”

Ability isn’t alone in pursuing high-bandwidth wireless transmission. Wairagkar says other fully-implantable BCIs, including both ECoG and penetrating-electrode designs, use similar high-bandwidth architectures, with some exceeding 50 Mb/s.

“Wireless implantable BCIs are still in early days, and we’re seeing multiple new clinical trials being conducted with these devices with varying capabilities in channel counts, signal-to-noise ratio, and data transfer rates, which will determine their performance, functionality, and long-term utility,” Wairagkar says.

Ability Neurotech’s BCI was tested during a brain surgery in Germany as part of the company’s first study involving a human patient. ABILITY Neurotech; TU Munich

Engineering Challenges of BCIs

Kopel says the implant had to meet several tight engineering constraints, including power and temperature limits. Moving that much data takes power and generates heat, making temperature control a key challenge.

Engineers also had to route the electrode connections into a hermetically-sealed case designed to keep moisture away from the electronics. The optical link must work through varying skin thickness, blood vessels, and hair, and tolerate imperfect alignment with the external headpiece.

Kopel says the technology took about 10 years to develop, followed by roughly 18 months of bench, durability, and other validation testing once the design was finalized.

The upcoming study will test those engineering choices over longer periods. Kopel says the company will first evaluate the implantation procedure and the device’s safety and performance. The next goal is to test whether participants can use the device to control a computer, followed by speech decoding—translating intended speech from brain activity into words in real time.

Long-Term Support and Durability

The harder test comes after implantation: keeping the system useful over years of daily life. Software needs updates, brain signals may change over time, and patients could remain dependent on an implant long after the company that built it has changed or disappeared.

Wairagkar says sustained performance and decoding accuracy over several years are critical for the long-term use of implantable BCIs. “Software challenges, like maintaining and calibrating the decoders and updating the user applications, are easier to solve, but it is important that the underlying signal quality obtained from the device is maintained over long periods,” Wairagkar says.

Fully-implanted wireless systems already avoid a major durability problem: the permanent connection through the skin used by some BCIs, Wairagkar says. Eliminating that connection can reduce infection risk and help with maintenance and everyday use.

Long-term support also depends on the company behind the implant. Kopel says Ability has planned for that possibility through its relationship with the nonprofit Wyss Center for Bio and Neuroengineering in Geneva, where the technology was developed before the company spun out in 2025. According to Kopel, if Ability ceased operations, its intellectual property and responsibilities would return to the center.

Wairagkar says the wider field will need stronger systems for supporting patients as implantable BCIs become more common. Groups are already examining questions around safety, access, and sustainability. Wairagkar added, “As the field matures, there will need to be structures and policies in place to support the use and deployment of implantable BCIs and to provide appropriate services to patients.”

When Ryan Hudson-Peralta, a self-described “Apple fanboy,” isn’t using the computer, he’s on his phone. But all of this screentime—and mouse use—can take a toll on his body. Hudson-Peralta, a designer, speaker, and disability advocate, was born with no hands and uses a wheelchair. “With my disability, I was born without shoulder sockets,” he says, and using a traditional computer mouse for an extended period leads to shoulder pain.

Through a consultancy he founded, Equal Accessibility, Hudson-Peralta sometimes requests test products, whether they’re designed for people with disabilities or not. In 2024, in exchange for feedback on the device, he received a prototype of Mouthpad, an experimental tongue-computer interface, similar to a touchpad on a retainer, which allows hands-free wireless interaction with computers and smartphones.

In addition to a touch-sensitive area on the roof of the mouth, the Mouthpad incorporates other assistive technologies: a barometer that detects “sip” gestures while also sensing tongue presses; and an accelerometer and gyroscope that track head movement. Designed to be as thin as possible to allow users to speak easily while wearing the device, it is compatible with dictation or voice commands. The device, which communicates via Bluetooth, is removable and rechargeable.

“The product is incredible,” says Hudson-Peralta. “I thought it was going to be a little bit of a learning curve, but it absolutely was not at all.” He still uses the Mouthpad, generally preferring to control a cursor with head movements and click with the tongue, in rotation with a computer mouse. For the first time in his life, Mouthpad has allowed Hudson-Peralta to do things on a computer away from the desktop, perhaps from the comfort of a couch, and give his shoulder a rest. He believes the device could have even greater benefits for others, for example, some of his friends with paralysis in their upper limbs.

Now, the Mouthpad may reach a wider audience. In July, it became commercially available to the public. Although the device has been developed prioritizing feedback from people with disabilities that can make it difficult to operate off-the-shelf electronics, such as quadriplegia, it is not being sold as a medical device.

“If you design for those who are most constrained, you design better interfaces for everybody,” says Tomás Vega, a cofounder of Augmental, developers of the Mouthpad.

Augmental/YouTube

A customized experience for all users

Mouthpad can connect to any device that accepts a Bluetooth mouse. It is expected to be used as an assistive device, directly or indirectly controlling a range of devices including tablets, smartphones, and even sexual aids—all tech used in everyday life that people with physical disabilities may have difficulty controlling.

Beyond its uses as assistive tech, Mouthpad could also augment workflows or video game experiences as a novel input option, find roles in occupational safety when both device access and free hands are important, and perhaps benefit scientific research. “We believe in universal design,” Vega says.

Over roughly six years of development, Augmental has added ways to customize Mouthpad inputs to match user preferences and abilities. The physical device also needed to be custom fit because of natural variation in mouth shape and size, and the hardware needed to be thin and flexible, yet durable. “The mouth is the most hostile environment in the body,” says Vega. Despite this, as long as users don’t grind their teeth, Vega expects the number of battery cycles to be the limiting factor for the lifespan of the device, at least two years.

A single Mouthpad costs US $1,400, which would not be covered by medical insurance, with additional fees for a dental fitting. As one Mouthpad reviewer notes, the device may be eligible for other forms of reimbursement, such as vocational rehabilitation programs.

Though less expensive computer input options may be available, Hudson-Peralta says that the Mouthpad is “much cheaper” than many of the assistive technologies that he uses and must pay for out of pocket. These devices do not generally have the same economies of scale as mass market electronics.

Vox tunes in on nearly silent speech

For many users, the Mouthpad will be one of multiple inputs for devices. Other hands-free computer controllers include mouth-operated joysticks, eye-tracking, and more experimental brain computer interfaces. Voice control is now a common method for inputting text, and along with the release of Mouthpad, Augmental announced a new necklace microphone device, called Vox. If Mouthpad is the mouse, the company says, then Vox is the keyboard.

Vox is currently able to understand “low-volume speech,” says Vega, but as with most other speech-recognition devices, it can struggle with outside noise or lack user privacy. One long-term goal for the Mouthpad, then, is a fully silent speech interface. That is, the ability to provide the benefits of modern speech interfaces without the need for users to speak audibly, even at a whisper.

Mouthpad currently only tracks the tip of the tongue; however, articulating certain speech sounds (e.g., k-, r-, and o-sounds) involves the main body of the tongue and the back of mouth, so silent speech may require following movement of the entire tongue. This whole-tongue tracking could open up therapeutic and research applications related to speech.

For example, some people can articulate their tongue and mouth but are unable to speak conventionally, perhaps due to a removed or damaged voice box. “These patients may experience social isolation and even depression due to their speech or sound disorders,” says Jun Wang, a speech scientist at the University of Texas, Austin, who has worked on a tongue-tracking device.

Expanding access to tongue-tracking tech

Augmental is not alone in its efforts to develop mouth-based interfaces.

Hananeh Esmailbeigi, for instance, is a biomedical engineer at the University of Illinois Chicago who develops wearable technologies, including a “tongue-trackpad.” Without the opportunity to examine the Mouthpad closely, Esmailbeigi declined to comment on specifics of the device, but applauded expanded access to the technology.

“I am encouraged to see growing attention and investment in this modality and greater public awareness of the potential of intraoral interfaces,” says Esmailbeigi. Beyond computer interaction, she is interested in the devices as a source of quantitative information in rehabilitation for motor control problems that affect speech.

“I think [tongue interfaces] have great potential due to the high flexibility and endurance of the tongue,” says Lotte N. S. Andreasen Struijk, a biomedical engineer at Aalborg University in Denmark. She helped develop iTongue, which is available in the European Union for computer and powered wheelchair control. Earlier versions of the device required a barbell-shaped tongue piercing to track the tip of the tongue; however, the newest version no longer requires the piercing.

Assistive devices like Mouthpad can facilitate computer interaction and build technical skills, says Hudson-Peralta. But he emphasizes that access to technology can “help people grow not just in careers, but in social life.”

This story was updated 13 August to clarify Jun Wang’s area of work.

This story was updated 21 August to remove “powered wheelchairs” as one of the devices that Augmental’s mouthpad can control.

This article appears in the October 2026 print issue as “A Touchpad for Your Tongue.”

Sixteen viruses is not a large number. But the 16 bacteria-infecting viruses described on 6 August in Science were no ordinary specimens.

They were not fished out of a sewage outflow or dug up from a soil sample, which is where such things normally come from. They were written by a genomic language model trained on vast troves of DNA sequences. Researchers at Stanford University designed the small viruses from scratch, producing the first complete, functional genomes ever generated by AI.

And they worked. Delivered together as a cocktail, the designer viruses—known as bacteriophages, or phages—infected E. coli strains that had already evolved resistance to the natural virus they were modeled on, something a comparable mix of natural phages could not do.

The advance offers a glimpse of a future in which bespoke phage therapies are made to order to combat bacterial infections that antibiotics can no longer touch.

Phage therapies have been used to treat infectious diseases for more than a century, but the field has struggled with a combination of biological and commercial hurdles: Individual phages often kill only a narrow range of bacteria, resistance can evolve quickly, and naturally occurring phages can be difficult to patent.

AI-designed phages offer a way around some of those limitations—and Brian Hie, the Stanford computational biologist who led the new study, says collaborators have already begun asking to use their model to create phages capable of killing disease-causing bacteria, rather than targeting a laboratory strain of E. coli.

But the same AI methods also lower the technical barrier to building other kinds of biological agents on demand, including viruses with the potential to cause disease, sharpening a long-standing worry that systems developed for medicine and biotechnology could be turned, without much modification, into biological weapons.

“The question is no longer whether generative viral genome design will exist,” a pair of biosecurity experts at the Johns Hopkins Center for Health Security wrote in an accompanying commentary. “It is whether society can build oversight that allows its benefits to unfold while preventing it from enabling serious harm.”

How the Phages Were Made

Inside the phrase “designed from scratch” sits a long engineering pipeline.

The researchers used their Evo 2 foundation model, which was trained on a dataset that included more than 2 million bacteriophage genomes. But for this experiment, the researchers further focused the model on the particular kind of phage they wanted to build—a redesigned version of a much-studied bacteriophage called ΦX174—by fine-tuning it on an additional set of some 15,000 genomes from the target phage’s own relatives.

They then added computational constraints and quality-control filters to maximize the chances that the AI-generated ΦX174-like sequences would produce working phages. That process yielded 302 candidate genomes.

Seventeen of these could not be synthesized. Of the remaining 285, the vast majority still failed to infect and kill bacteria—the most basic function of any phage. Only 16 could ultimately be “rebooted,” meaning converted from synthetic DNA sequences into infectious, bacteria-killing phages.

The result shows that machines can, in fact, write functional viral genomes, albeit relatively small ones containing just 5,400 DNA letters and only 11 genes. But considering the painstaking process it took to produce those 16 working phages, it’s worth asking what exactly the AI contributed, and what would have to change before the method could yield a truly dangerous human pathogen.

“Right now, I think it would still take a lot of work,” says Hie, who holds a joint appointment at the Arc Institute in Palo Alto, California. “It would definitely require a very talented interdisciplinary team to do this at the moment,” he says—never mind the $100,000–$200,000 in DNA synthesis costs that Hie estimates the project would have cost his team if they had to pay market prices. (Twist Bioscience provided the service at a discount.)

Hie continues: “Every single virus that you want to reboot in the lab is different and has different experimental conditions that need to be optimized. It needs a lot of domain-specific expertise.” Plus, he adds, “We don’t have a sufficient understanding of how the genetic changes proposed by the AI system lead to improved pathogenicity.”

How New Are These AI-Designed Phages?

Before looking too far ahead at what AI-designed viruses might become, it’s also worth asking how much novelty these viruses actually represent.

An independent analysis of the Stanford data—led by Oliver Crook, a computational biochemist at the University of Oxford—found that the 16 viable phage genomes were on average about 97 percent identical to their ΦX174 template. Placed on a family tree, the AI-designed viruses fell inside the existing spread of phage diversity, rather than branching away from it, Crook concluded.

In other words, the model was mainly rearranging familiar genetic material into new combinations. “What we saw, at a very plain view, were brothers and sisters of the original virus,” says Crook. “They’re not fundamentally behaving in a new way or using molecular mechanisms that they didn’t before.”

Sequence novelty, however, does not tell the whole story. Several of the AI-generated phages differed from ΦX174 in their three-dimensional protein structures, growth kinetics, and infection dynamics—properties that ultimately determine how a virus behaves, notes synthetic biologist Samuel King, a graduate student in Hie’s Laboratory of Evolutionary Design and the paper’s first author.

For example, one of the designed viruses carried an unusually truncated protein that packs DNA into new viral particles. The AI had borrowed this protein from an evolutionarily distant phage and made it work on the ΦX174 genomic backbone by rewiring the surrounding DNA. Notably, an analogous gene swap had previously been shown to be nonviable when introduced into ΦX174 through conventional genetic engineering. “That’s quite a new configuration,” King says.

For Chase Beisel, a chemical engineer at the Botnar Institute of Immune Engineering, in Switzerland, and cofounder of the phage therapy company Locus Biosciences, such moments show where the real promise of AI-designed phages lies: not in conjuring viruses wholly unlike anything in nature, but in searching through combinations of genetic changes that evolution has never produced and that scientists might never think to test.

“It’s a novel way to explore sequence space and uncover new attributes,” he says. “That’s going to be really useful in the long run.”

This article appears in the October 2026 print issue as “Should We Worry About AI-Designed Viruses?.”

On 12 August, just before sunset, a strip of the Iberian Peninsula will witness its first total solar eclipse in more than 120 years. For many across the region, this is a rare chance to see the moon block out the sun. For one group of researchers, it’s an even rarer chance to record the human body’s reaction to that awesome phenomenon.

For the first time, scientists will monitor those observers with the aid of smartwatches and fitness trackers. In Catalonia, the Solaris project is recruiting wearable device owners to record how their hearts respond to the darkening of the sun. Solaris is an acronym in the Catalan language that translates in English to “Monitoring of Observations of Cardiac and Respiratory Activity during a Solar Eclipse.” The project should log thousands of physiological responses to the eclipse across a vast area.

Solaris is just the latest example of how medical researchers are turning to wearables for data that can’t be gathered in a lab. “In the past, it has been that you needed somebody to be in the laboratory to be able to collect data on them, and we know that’s just not a great representation of what people’s behaviors and physiology are in everyday life,” says Jessilyn Dunn, a biomedical engineer at Duke University, who isn’t involved with Solaris. “Wearables provide us a window into what’s really going on.”

Solaris App Tracks Eclipse Heart Data

Indeed, no Solaris participant will set foot in a lab. The project is open to anyone with a smartwatch, fitness tracker, or another device that can measure its wearer’s heart rate. Those who wish to take part can download the Solaris app, available for both iOS and Android. Several thousand people have already done so.

As the eclipse nears and as those device-wearers go about their business, the app will record their heart rates and breathing patterns over a five-day period. That span includes the day of the eclipse itself, as well as two days before and two days after to establish a baseline. All data should be anonymized.

After the eclipse, medical researchers at Barcelona’s Vall d’Hebron Research Institute will have a record of thousands of physiological responses to the same event from across Catalonia. Gathering a dataset like this would have been virtually impossible before the current era of widespread wearables.

In the future, if researchers want to study how lots of people physiologically respond to one event, they might look to Solaris for inspiration. “I suspect the long-term significance will extend beyond the eclipse itself,” says Michael Chee, a sleep researcher at the National University of Singapore who is also uninvolved with Solaris. “The same approach could be applied to heat waves, pandemics, natural disasters, major sporting events, shift work, or other societal phenomena that affect health and behavior.”

How Wearables Enable Large-Scale Research

The same wearable devices that track their users’ sleep schedules, exercise habits, and vital signs can, if users are willing, allow researchers to do the same—on a much larger scale. Other research groups already take advantage of the fact that the owners of wearable devices use them in their everyday lives for years on end.

“Wearables allow us to collect continuous, objective physiological and behavioral data over months or years in tens of thousands—or even hundreds of thousands—of people,” Chee says. “That is something that was previously impossible with traditional laboratory methods.”

Sleep researchers like Chee have benefited from the wearable era. Thanks to sleep trackers, they can now sift through millions of real-world nights at once. It’s become far easier to analyze phenomena like the global effects of jet lag or how different countries and age groups sleep differently.

Wearables have also catalyzed studies of physical activity, thanks to the ready availability of exercise logs from devices like Fitbits.

From Chee’s perspective, however, wearables are not perfect, and not only because people sometimes forget to charge their smartwatches. Today’s wearables were not designed as precision scientific instruments. In practice, this means that one manufacturer’s smartwatch might measure sleep and physical activity very differently from its competitor’s, which makes researchers’ jobs more complicated.

Efforts to standardize this are underway, but even if that does happen, wearables will probably never fully replace a proper lab for precise measurements. Instead, these devices’ real value to researchers comes from their ability to gather lots of data in the real world.

That will be easier as wearables on the market are rapidly growing more capable. “One of the really exciting things is that we can get more types of measurements, a higher frequency of measurements, more accurate measurements,” Dunn says. “That’s improving every day, every year.”

When you get a good night’s sleep, you aren’t just giving your brain a chance to rest. Sleep activates a system only discovered in 2012 that washes out brain waste. Called the glymphatic system, it’s comparable to the better-known lymphatic system that moves and filters fluids throughout your body. A healthy glymphatic system is linked to good cognitive function and could prevent neurodegenerative diseases like Alzheimer’s, but monitoring it during sleep has been practically impossible in humans, because today’s methods require noisy, confining MRIs and invasive spinal injections.

A new wearable device developed by researchers at Georgia Tech and Seoul National University (SNU) could offer a safer and more sleep-friendly alternative. The technology shines near-infrared light to detect brain water, a soup of the fluids that constantly flood your brain. The brain-water mixture contains cerebrospinal fluid (CSF), which is what the glymphatic system uses to flush out waste particles like plaques that block in-brain communication. Measuring total brain water could be a way to study how the glymphatic system moves CSF around to clean the brain, researchers say.

The patch, the design of which was published this month in Science Advances, is intended for ease of use. It’s the size of a Band-Aid and less than a centimeter thick. Its soft silicone body conforms to the user’s forehead, and it doesn’t require a wired connection during sleep. Plus, it can be recharged and used over multiple nights, capturing more long-term information than what traditional sleep studies and MRIs can.

“MRI is superexpensive, it’s not really accessible, and more importantly, you cannot sleep under MRI imaging,” says W. Hong Yeo, Peterson Professor in pediatric research at Georgia Tech. “With our device, we can naturally capture conventional sleep right at home.”

What brain water could say about sleep and the glymphatic system

The glymphatic system is essentially a network of tiny voids between veins, arteries, and cells that are flooded with CSF when brain cells relax. Sleeves called perivascular spaces, which surround blood vessels, deliver the fluid to the spaces between cells. Dr. Chang-Ho Yun, a professor of neurology at SNU’s Bundang Hospital, says that these intercellular spaces can expand by about 60 percent in sleeping mice, but observations of the change in humans remain indirect.

“The human brain is densely packed with cells,” Yun says. “During sleep, the alerting signal [noradrenaline] drops away, the cells shrink, and there’s room for cerebrospinal fluid to flow.”

The new device uses a simple light trick to try to detect CSF changes. If you’ve ever held a flashlight to your palm in a dark room, you’ve seen the beam cause your hand to glow red. That’s because shorter wavelengths of light, like green and blue, are absorbed in your tissue. Red light breaks through and scatters back.

The patch shines three different wavelengths of near-infrared light: two that are absorbed by blood-cell proteins called hemoglobins, which deliver oxygen to the brain, and one that is absorbed by water. What’s scattered back is picked up by the device’s photodetector. The pattern of absorption at each wavelength reveals how much blood and total brain water lie along the path of the light. Yun says that if total brain water rises while hemoglobin stays flat, the added water is not coming from blood, which could mean CSF is increasing and the glymphatic system is doing its job. This indirect measure is necessary because CSF does not reflect light all that differently from the other fluids in your brain.

“Although it is indirect evidence, it’s compatible with known theory and known facts demonstrated in animals and humans,” Yun says. In his previous research, he found evidence that suggested glymphatic activity lowers during the REM stage of sleep. The study associated with the new patch, which measured sleep in four people, showed brain-water measurements doing the same during the REM cycle. Still, he emphasizes that further research is necessary to determine if total brain-water changes truly signal glymphatic activity.

Lauren Hablitz, an assistant professor of translational neuromedicine at the University of Rochester, agrees that it’s hard to say whether the patch is actually monitoring the glymphatic system. Knowing for sure might even be impossible, she adds. “The brain is bathed in fluid, it sits in fluid, it floats in fluid,” she says. “Knowing whether it’s that pool of fluid or the perivascular space or the ventricles that’s changing is hard.”

But Hablitz, who was not involved in this project, is optimistic about how the new tech can be used, even if it isn’t ultimately measuring the glymphatic system. She says that sleep research is “heavily focused” on electroencephalograms, or EEGs, which use electrodes placed on the scalp to measure the brain’s electrical activity. Yet most people with sleep issues have normal EEG readings, she says.

“Maybe something like this patch, that can look at another aspect of the biology that isn’t just neuronal activity, can start saying something about what’s actually happening in sleep disruption,” she says.

Supplying power to bioelectronic implants is essential for long-term operations. However, placing a socket on them can invite invasions from microbial intruders, and wireless charging antennas are often bulky. Now scientists have created a new implantable power outlet accessible via needle they say can overcome the challenges faced by previous charging techniques.

“It is intended to serve as a general access point that could be integrated with many existing implantable bioelectronic devices, including sensors, neural interfaces, stimulators, and battery-powered systems,” says Dion Khodagholy, an associate professor of electrical engineering and computer science at the University of California, Irvine.

The new device, which the researchers call an implantable bioelectric outlet, is made mostly of a soft spongy plastic with pores about 150 micrometers wide, comparable to a very fine needle’s diameter. They first dipped the sponge in a highly electrically conductive polymer to coat its pores with a layer of the polymer 100 to 200 nanometers thick. Next, they dipped the sponge in a silicone rubber solution to provide a protective electrically insulating jacket around its exterior. Finally, they sandwiched several of these jacketed sponge layers between unmodified layers of the sponge and covered the entire stack with silicone rubber to form a module useful for applications.

“With our approach, the device remains completely under the skin,” says Hyung Joon Shim, a postdoctoral scholar in electrical engineering at UC Irvine. “A needle is inserted only when electrical access is needed and is removed afterward.”

Implant Charging With a Jab

In experiments on mice and rats, the scientists coupled their outlets with implants such as neural interface devices. By inserting a needle into the outlets, the researchers could recharge the batteries of the neural interface devices as well as receive data from them at the implants’ maximum transfer speeds of nearly 16 megabits per second. They detailed their findings 8 July in the journal Science Advances.

In addition, in experiments on pigs, the researchers combined their outlets with implants capable of delivering electrical stimulation, which previous work found could help boost nerve healing. The scientists could deliver 20-microampere electric pulses, each lasting 100 milliseconds, for an extended period using the outlet to help keep the electrical stimulation going.

The porous, resilient nature of the device prevented cracks and tears from growing and spreading when the device was poked with a needle, even with repeated jabs with needles ranging from 30 gauge (0.1 millimeter wide) to 18 gauge (1.5 mm wide). “In our laboratory tests, the device maintained its electrical performance and insulation after more than 100 insertions,” Khodagholy says. In addition, the outlet’s jacketed layers are electrically conductive throughout, so a needle “does not need to hit one very small point to make a connection.”

The scientists had their outlets implanted in mice for more than a year. The outlets did not degrade during that time, nor did they cause any visible problems for the rodents. “Long-term safety and stability are among the most important requirements for any implantable technology,” says Jennifer Gelinas, an associate professor of pediatrics and anatomy and neurobiology at UC Irvine.

Refining the Outlet for Real-World Use

In the experiments, the animals were anesthetized, which helped keep the charging needles stable. “For use in an awake subject, the needle and attached wire could be secured to the skin with medical tape or an adhesive dressing, similar to an IV needle, to prevent movement during charging or data transfer,” Gelinas says.

Passing a needle through the skin to connect with the outlet “would probably cause some brief discomfort, similar to an injection or a small IV needle,” Gelinas notes. “Future versions could use smaller needles, topical anesthetics, or coatings that reduce pain and inflammation.”

In addition, “the implantable bioelectric outlet probe would not need a hollow channel for delivering fluid, which means that a dedicated implantable bioelectric outlet needle probe could potentially be made even thinner than a conventional injection needle,” Gelinas explains. “That may further reduce pain and irritation.”

The researchers note that their outlet could be used together with wireless technology. “For example, an implant might use wireless communication for routine operation, and use the implantable bioelectronic outlet only when direct access is more useful, such as for fast charging, downloading large amounts of data, updating the device, or performing maintenance,” Shim says.

The researchers fabricated the outlet “relatively easily using readily available materials,” Shim adds. “That could make future manufacturing and translation more practical.”

The scientists caution that their device is not yet ready for use in patients. “We would need to evaluate pain, skin irritation, infection risk, tissue response, and whether the electrical performance changes after many access sessions,” Gelinas says.

Researchers in China have developed a soft exoskeleton for the lower body that is completely motor-free and is driven instead by high-power artificial muscles. The harness reduces the amount of energy a person spends walking by nearly a sixth, surpassing most hip-assistive exoskeletons, according to a study detailing the device published 10 July in the journal Science Advances.

Companies and research groups worldwide are developing soft exoskeletons as wearable harnesses that can assist and augment tasks such as walking, lifting, and running while not restricting natural, comfortable movements as rigid exoskeletons do. However, soft exoskeletons often rely on bulky rigid electric motors or pneumatic actuators, which hinder the user’s mobility.

For decades, scientists have sought to create artificial muscles from polymers known as dielectric elastomers. These soft, lightweight, flexible, stretchable materials change shape when a voltage is applied, much as the human body’s muscles contract or expand because of electricity.

However, dielectric elastomer actuators have faced a number of challenges in exoskeleton applications. For instance, they often experience a trade-off between their electrical and mechanical properties—either they require a lot of electricity to drive their motions, or they are mechanically weak. In addition, previous artificial muscle fibers made of rolled-up sheets of dielectric elastomer were typically bulky, limiting their ability to conform to body contours.

Soft Hip Exoskeleton Uses No Motors

In the new study, Wei Yu, an associate professor of mechanical engineering at Hebei University of Technology in Tianjin, China, and his colleagues experimented with a dielectric elastomer rubber made of compounds that were highly polar—that is, within them, electric charges were highly separated. They next added a highly polar compound that helped molecular bonds form within the rubber to augment its mechanical strength, Yu says. At the same time, the high polarity of the compounds within this material helped make it more responsive to applied electric fields, he adds.

The scientists rolled stacks of thin films of their new material to form cylindrical fibers as little as 850 micrometers thick and as much as 250 millimeters long. In tests, soft, threadlike fibers of the new material only 1.95 millimeters wide could lift more than 400 grams, or more than 1,300 times their own mass. All in all, these new fibers are thinner and longer than previous artificial muscle fibers made of rolled-up sheets of dielectric elastomer, and nearly 10 times better in terms of output, the researchers say.

A volunteer clad in the new exoskeleton wears sensors to monitor metabolism while walking on a treadmill.Ziqi Zhang, Wei Yu, et al.

The scientists developed multifiber bundles that could plug into devices like Lego pieces. They next incorporated two 10-fiber bundles with a mass of just 6 grams into a soft hip exoskeleton. When the leg extended during walking, an applied voltage caused the fibers to lengthen and store elastic energy, which was released to assist the leg swinging. All in all, if a person is walking 4 kilometers per hour, the harness reduces the energy a person spends walking by an average of 13.9 percent compared to no assistance, the researchers found. This outperforms most previous hip-assistive exos.

Limits of Dielectric Elastomer Actuators Tested

Yu notes the team’s new exoskeleton was tested under lab conditions. “Its long-term stability and reliability still need to be further verified in more complex real-world wearable environments, such as under prolonged continuous movement, exposure to sweat, temperature variations, and differences in individual human motion,” he says.

In addition, Yu cautions that dielectric elastomer actuators typically require high driving voltages of more than 1,000 volts. “How to further reduce the operating voltage while maintaining high output performance remains an important direction that we are particularly interested in,” he says.

In barely a decade, the continuous glucose monitor has turned real-time blood sugar into a number that millions track on their phones. The small round patch on the back of the arm is everywhere now—on diabetics, athletes, biohackers, and the simply curious. And for people with diabetes, it has revolutionized management of their disease.

Now a startup called Adaptyx Biosciences has taken a major step toward doing for the stress hormone cortisol what the glucose sensors did for blood sugar. Last month, at the American Diabetes Association’s annual meeting in New Orleans, Adaptyx scientists reported what they say is the first continuous measurement of free cortisol drawn straight from human skin.

Using a matchbook-size patch studded with sensors, the team tracked cortisol in the fluid just beneath the skin, catching its daily swings as they happened—and opening a window onto a biological signal that governs metabolism, sleep, immune function, blood pressure, and much more.

Though the human body is complex, with many interconnected systems and signals, cortisol plays a role in regulating many physiological processes. “We’re measuring the hormone that is literally at the core of your entire biology,” says Elena Christofides, an endocrinologist in Columbus, Ohio, who consults for Adaptyx.

How the Patch Reads a Hormone

At the heart of each sensor is an aptamer: a short, folded strand of synthetic DNA engineered to latch onto a single molecule, in this case cortisol, and flip a tiny electrical switch when it does. As cortisol drifts in and out of the fluid, the switches flicker on and off, and the pattern becomes a running readout of the hormone’s level.

In one test, three healthy volunteers swallowed a dose of hydrocortisone, a synthetic form of cortisol. The patch traced the resulting spike and decline in step with gold-standard lab measurements gathered from blood samples drawn every half hour. In another set of volunteers, the device watched cortisol overnight and caught the hormone’s natural choreography: the low point in the small hours, the sharp climb that arrives with waking, and even the disrupted rhythm that comes with a night spent staying up late and drinking booze.

It was a small proof-of-concept study, and the company has a way to go before it can turn the prototype device into a tool that delivers medically actionable readings a doctor would trust. But it is the kind of result the field has been chasing for years, says Jason Heikenfeld, an electrical engineer who directs the Novel Device Lab at the University of Cincinnati.

“It’s super exciting,” he says. “Everyone has been wanting to go past glucose.” But the chemistry always fell apart under the strain of real-world use. Now, after years of refinements to aptamers—including advances in stability, surface coatings, and signal processing—the engineering is catching up to the ambition.

“We’re finally at a point where all of these technological advances are converging,” says Ula Rustamova, cofounder and CEO of Level Zero Health, another startup developing aptamer-based sensors for continuous monitoring of cortisol as well as reproductive hormones such as progesterone.

No diagnostic aptamer-based device has ever been cleared by the U.S. Food and Drug Administration. But with the first convincing human results now in hand, Heikenfeld—who doubles as CTO of Kilele Health, a company he cofounded to develop aptamer-based sensors for real-time monitoring of phenylalanine, cortisol, and other biological signals—expects continuous cortisol monitors to reach the market before the end of the decade.

“We’re just a couple years off,” he says.

From Adrenal Disease to Diabetes

According to Adaptyx cofounder and chief science officer Alex Yoshikawa, the company plans to aim its first product at people with rare adrenal disorders in which cortisol runs clearly too low or too high.

In Addison’s disease, for example, the body makes too little of the hormone, leaving patients reliant on cortisol pills and at risk of a dangerous crash. A continuous monitor could help doctors fine-tune those doses in real time, the way a glucose monitor guides insulin.

The larger target, however, is diabetes. Around one-quarter of patients with hard-to-control type 2 diabetes are thought to carry a hidden surplus of cortisol that raises their blood sugar. A continuous cortisol monitor could catch that hidden cortisol excess, in principle steering patients toward the right treatment instead of endless medication increases.

But cortisol is only the beginning. “We’re building this to be a platform that is generalizable for many different types of use cases and molecules,” says Yoshikawa, who helped originate much of Adaptyx’s core technology as a graduate student in the laboratory of Stanford electrical engineer Tom Soh.

Adaptyx’s sensor already has an array running up to 16 channels in parallel. Because each channel’s target is set by its aptamer, swapping in a different one could, in principle, retune the sensor to a different molecule. Yoshikawa says his team has done exactly that with creatinine, a marker of kidney function that today can only be checked with a blood test.

A future version of the platform could track a broad panel of hormones or metabolites simultaneously, he points out, creating a continuous picture of the body’s chemistry that no snapshot blood draw could assemble. “We see cortisol as the stepping-stone for general hormone monitoring,” Yoshikawa says.

A Patchwork of Rivals

Adaptyx is not alone in chasing that vision. Biolinq, the company behind a color-coded glucose sensor patch, has begun exploring opportunities in multi-analyte monitoring, with cortisol a leading candidate.

Others see cortisol as but one target among many. Level Zero Health, for example, is prioritizing the continuous monitoring of luteinizing hormone, a reproductive hormone that triggers ovulation and could pinpoint the fertile window for women trying to conceive.

Then there are companies, such as EnLiSense, that already have cortisol-tracking wearables on the market—though such devices read the hormone from sweat, not the subdermal fluid as Adaptyx and others do. Sweat is far easier to reach, since the sensor never has to breach the skin, but is a less faithful mirror of the blood, notes Taeil Kim, a mechatronic systems engineer at Baylor University who has developed sweat-based cortisol sensors.

That trade-off buys convenience, Kim says, at the cost of accuracy. It also lets devices be sold as wellness products rather than face the stricter bar for medical clearance.

Though there is little evidence that continuous cortisol tracking improves the health of people without a disease diagnosis, and doctors generally see scant reason for the healthy to monitor it—or to track glucose, for that matter—the commercial appeal of the wellness market remains strong. That has entrepreneurs racing in.

Uroš Kuzmanović, for one, hopes to tap into that growing consumer demand for health tracking with a cortisol sensor sold directly to the public—athletes chasing better recovery, workers worried about burnout, and anyone curious about how stress registers in their body.

Founder and CEO of BioSens8, Kuzmanović points to the popularity of wearables like Oura rings and Whoop bands, which estimate “stress” and “recovery” from indirect measures such as heart rate, heart-rate variability, and temperature, as evidence of interest in physiological metrics that a cortisol sensor could measure directly. But rather than relying on algorithms to infer stress from these surrogate signals, by measuring the hormone from fluid under the skin, “we could have a more direct, more accurate readout of what is happening with your body,” he says.

The rise of glucose monitors proved that a hidden number, made visible, could change how millions manage their health. Cortisol tracking may be next, thanks to a maturing set of tools for reading hormones continuously without a blood draw.

But precision is not the same as insight, and whether continuous cortisol measurements deliver meaningful improvements in how people manage their health is a question no sensor has yet resolved.

Shaped like dogs, stars, and the Mona Lisa, you could mistake these DNA structures for fun-shaped macaroni if they weren’t only nanometers wide. South Korean scientists made the constructions using a technique called DNA origami, which can bend genetic material into any form. Designing DNA strands so they’ll fold into a specific shape typically requires tedious manual work, but the researchers behind the playful fabrications have developed a shortcut using generative AI.

The AI model, called Generative SNUPI (short for Structured Nucleic Acids Programming Interface, and, yes, inspired by the dog), was created by research teams at Seoul National University (SNU) and Hanyang University. The work behind it, which was accepted for publication in Nature Communications, shows the model can conjure DNA origami designs that work in the real world for user-requested shapes. For a design like the Mona Lisa, that doesn’t mean simply tracing an outline; the model considers the chemical rules of DNA to tell researchers how unpaired DNA strands should be sequenced so that molecular forces will cause them to self-contort into the required shape.

DNA origami techniques have been around for two decades now, with potential applications ranging from nanoscale robots to therapeutic structures that interact with cells. But these innovations have been slowed by how time-consuming and expensive the DNA structure design process can be.

“Traditionally, we need some expertise, background knowledge, and know-how to design the proper nanostructures that we intend to make,” says Kyounghwa Jeon, a Ph.D. candidate at SNU. The work requires humans running algorithms and tweaking results until the desired shape is achieved and structurally stable. With Generative SNUPI, she says, users could, in theory, go straight from drawing a target shape to physically assembling the DNA.

Rebecca Taylor, a professor of mechanical engineering at Carnegie Mellon University who was not involved in the research, says the new generative platform is exciting for researchers. “The entire field is sort of enabled and held back by its tools. When you make a new tool that enables a new tech, a new capability, that’s just such a big advance for the field.”

Generative SNUPI designs DNA sequences that, when synthesized, fold into nanoscale replicas of user-requested shapes.Source images: Chien Truong-Quoc, Kyounghwa Jeon, et al.

How AI can design DNA origami

Designing DNA origami using Generative SNUPI begins with a target shape. That could be something with complex curvature, like the outline of a dog’s face, or a more simple geometric pattern. Next, the new tech comes into play: Generative SNUPI applies a diffusion model, which adds and refines noise to the input shape to create the desired output in DNA form. Diffusion models are how platforms like DALL-E and Midjourney create AI-generated imagery.

“What it looks like is one of those kids crafts, where you decorate something with glue and then put glitter all over it,” says Taylor. When the noise is removed—or the glitter is shaken off—the design is revealed. “They’re basically just saying ‘populate this guide that I have with the DNA,’ but they also know how DNA comes together. … That’s the thing that it’s really been trained on.”

The arts-and-crafts metaphors only continue once Generative SNUPI returns the DNA sequences that form the target shape. Scientists chemically synthesize short DNA strands called staples and used biological methods to produce a long strand called a scaffold. The staples pull the scaffold into shape in a way that Jeon says is “very similar to stapling paper.” The staple-scaffold relationship exploits DNA’s imperative to bond guanine to cytosine and adenine to thymine; the exact positions of each of these molecules are dictated by Generative SNUPI during the design process.

Researchers were able to produce a variety of DNA origami structures, but some did not hold their shape at first, notes Do-Nyun Kim, an assistant professor of mechanical engineering at SNU. “This occurred not because Generative SNUPI had an error, but because the drawn shape was, in fact, structurally unstable,” he says. In response, they added a step before actually designing the DNA sequence to predict the structural integrity of the input shape.

To expand Generative SNUPI’s capacity for real-world applications, Kim says that DNA origami designs will need to be less rigid than what the model is currently able to produce. The technology reaching its full potential could mean life-saving uses like drug delivery and immunotherapy, but these uses often require flexibility.

“Most molecular structures are dynamic and reconfigure in response to external stimuli to perform their designated functions,” he says. “So, we plan to extend the current work to the design of dynamically reconfigurable structures in future research.”

This article appears in the September 2026 print issue as “AI Model Folds DNA Into Mini Mona Lisas.”

For more than a decade, artificial intelligence has been touted as a way to dramatically accelerate drug discovery. Yet despite billions of dollars in investment, relatively few AI-designed medicines have made it to patients. That’s partially because the timelines for careful drug testing can’t be easily compressed—and partially because drug development is just really hard.

Isomorphic Labs, the Google DeepMind spin-off that’s building on DeepMind’s Nobel Prize-winning work on protein structure prediction, may be making the most progress. The company has signed major drug-discovery partnerships with Novartis and Eli Lilly and recently raised US $2.1 billion in funding. In February, it published a technical report describing its new Isomorphic Drug Design Engine, a system created to discover the “pockets” on proteins where drugs can bind and in general to predict how proteins and drug molecules interact.

IEEE Spectrum spoke with Adrian Stecuła, a group leader in the machine learning organization at Isomorphic Labs, about how close AI may be to becoming a practical tool for designing new medicines.

Going Beyond AlphaFold

AlphaFold2 and AlphaFold3 were massive leaps forward for computational biology. Why weren’t those models sufficient for actually designing drugs?

Adrian Stecuła: AlphaFold2 was eventually recognized with the Nobel Prize, because it arguably solved the problem of protein folding. But proteins don’t exist in a vacuum, right? They interact with a wide variety of other types of biomolecules, which involves nucleic acids, small molecule ligands, ions, and other proteins. AlphaFold3 introduced a way to model the rest of these cellular biomolecules as part of a single framework. So all of a sudden, we have a single model that can model all of these interactions all at the same time.

That said, in the years since the AF3 release, multiple groups have evaluated it along the axis of pocket novelty. And you could see that as the pocket distance grows away from the training set, the model performance decreases. So if you define the success as “how well did the model actually fold this particular ligand with this particular protein,” as those systems become more novel, you can see a decline in performance.

But for drug discovery, ideally we do want to pursue novel mechanisms of action, which might involve targeting a never-before-observed pocket. And so it is absolutely important for us to have our models generalize to these regions that are distant from training.

How does the Isomorphic Drug Design Engine (IsoDDE) address these limitations, and what exactly is it predicting?

Stecuła: It takes a lot more than just structure prediction to create a molecule that will ultimately become a drug. You don’t just need to predict where the ligand binds with the protein, but also potentially how it binds, how tightly it binds, and a plethora of other properties about the ligand and how the ligand interacts with the rest of the proteins in the body.

IsoDDE is a unified computational system that lends itself to a number of different endpoints. As part of the technical report about IsoDDE, we have described three of those endpoints, which are structure prediction, pocket identification, and binding affinity prediction. [Editor’s note: Binding affinity measures how strongly a molecule binds to a target protein.]

Finding Hidden Protein Pockets

In your technical report about IsoDDE, you highlighted a key example involving a protein called cereblon and its “cryptic pocket.” First, can you tell readers about this protein and what a cryptic pocket is?

Stecuła: Cereblon is one of the most important and well-studied proteins in the targeted protein degradation pathway. It’s part of the mechanism that’s responsible for degrading proteins in the cell. [Editor’s note: Some drugs use cereblon to mark disease-causing proteins for destruction by the cell.]

And cryptic pockets are pockets on protein surfaces that are non-obvious, in that in the [unbound] state of the protein, meaning that if you were to look at the protein by itself, it would not have a cavity there. This pocket only opens upon the binding of just the right ligand. So you can think of it as: You need the perfect key to unlock this lock.

How did you use recently published findings about cereblon to validate IsoDDE?

Stecuła: In January of this year a Nature paper published a completely novel, never-before-observed cryptic pocket on the surface of this protein. First we asked the question: Can IsoDDE find this pocket just using the protein sequence as input? And we were able to perfectly predict the location of this cryptic pocket. Again, note that this pocket had never been disclosed before.

The second question was: Can IsoDDE accurately predict how the ligands bind to the protein? Are we able to recapitulate the crystal structure shown in the Nature paper? And the model was able to place both the orthosteric ligand [at the known binding site] as well as the allosteric ligand [at the new, cryptic pocket] in exactly the perfect locations.

Most drugs today are small molecules—relatively simple compounds that bind to proteins. Does IsoDDE expand the toolkit for tackling diseases?

Stecuła: I think many of the hopes for machine learning for drug design revolve around making more protein targets tractable. There are already many diseases that have known associated proteins. So we know that if only we could target a particular protein, we would have a chance at helping the patient population suffering from a particular disease.

But in many of those instances, the protein that is associated with that disease doesn’t have a pocket or mechanism that can be easily drugged. IsoDDE is enabling us to find those mechanisms. Further, these methods generalize not just to small molecules but also to antibodies, molecular glues, and peptides. It’s not just a breakthrough that will impact small molecule design, but these other therapeutic modalities will also benefit from this.

There is a lot of hype around AI in drug discovery. What do people commonly misunderstand about where the field is right now?

Stecuła: Perhaps the misunderstanding is that just because we are able to accurately model structure, that drug discovery is a solved problem. It does take, we believe, a unified system such as IsoDDE with a plethora of other endpoints to really model these systems. We will continue to improve our performance on the endpoints that we have disclosed as part of the IsoDDE report, and will also continue to push on the endpoints that we have not yet disclosed.

Do you imagine more of the drug discovery process becoming automated, with AI systems generating hypotheses, testing hypotheses, and analyzing results?

With the reliability of a quality wristwatch, pacemakers send out electric pulses to keep your heart beating at a steady rate. But unlike a watch, when the batteries need replacement, it’s a surgical affair—one that can be required as often as every five years. While the risks of having a pacemaker implanted are low, going under the knife always creates the potential for complications.

A group of California and Massachusetts scientists have developed a pacemaker that works without requiring surgery, publishing their work last week in Nature Biomedical Engineering. Researchers developed a wearable alternative to the traditional pacemaker measuring in at about the size of an iPod Shuffle. The device sticks to the patient’s chest, putting out ultrasound waves that tell the heart to beat.

But the ultrasound technology is only one component of what makes the noninvasive pacemaker work. Patients would undergo a gene therapy procedure to help heart cells react to the high frequency waves. Delivered by a simple injection, the treatment would work like a signal booster for the waves of the ultrasound device.

The approach has proved effective in rats, pig hearts—used for their similarity to human hearts—and samples of human heart cells.

“This is a very innovative and exciting study,” says Dr. Roger J. Hajjar, who heads the Gene and Cell Therapy Institute at Mass General Brigham and was not involved in the work. In terms of demonstrating that the technology could be safe and effective, he says, “the work is impressive.”

How an ultrasound pacemaker would work for patients

In a clinical setting, treatment would begin with a gene therapy injection that helps heart cells “hear” the ultrasound signals. It works by prompting the cells to produce a sound-sensitive protein in the ion channels that dot their membranes. The broad term for this type of therapy is “sonogenetics”—priming cells to respond to sound. It’s the same idea as a type of gene therapy that makes cells react to light, called optogenetics, which has been studied for treating hearing impairment and pain.

Importantly, this gene therapy doesn’t alter DNA, says Gengxi Lu, who was a mechanical engineering postdoctoral researcher at MIT while working on the paper and is now a senior ultrasound engineer at Meta. Rather, he says, the treatment introduces RNA into cells that directs them to create the ultrasound-sensitive protein without altering their genetic code.

A wearable device would then be stuck to the patient’s chest like a bandage. The device connects by wire to a data and power module that can be placed in a pants pocket. It would look similar to an insulin pump.

The ultrasound patch is programmed to emit high frequency waves that the cell proteins receive. These signals stimulate the cells’ ion channels to let in calcium. This influx of calcium ions cues the heart to beat.

Is sonogenetics a viable solution?

In terms of future research, sonogenetics is ripe for solving health issues, says Chen Gong, who was a Ph.D student at the University of Southern California while conducting the research and is now a postdoctoral researcher at MIT. Researchers from Harvard; the University of California, Los Angeles; and Caltech also contributed to the research.

Source images: Chen Gong, Qifa Zhou, et al.

Using the sonogenetics technology, “we can modulate almost anywhere we want to stimulate, like in the inside of brains, eyes, other organs,” he says.

But the ultrasound pacemaker still needs to prove itself. For the device to be clinically practical, Dr. Hajjar says, it would have to be comparably reliable to traditional pacemakers under limiting factors like exercise, long-term use, and anatomical differences. Additionally, gene therapy, though FDA approved in some contexts, is a hurdle in terms of costs, safety, and regulation.

“The biggest question is not whether the ultrasound device works—it appears quite promising—but whether the benefits of a noninvasive pacing system are large enough to justify exposing patients to cardiac gene therapy when current pacemakers already have excellent safety and performance,” he says. “That will ultimately determine the size of the clinical opportunity.”

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.

This article appears in the August 2026 print issue as “Future Hearing Aids Will Know Who You Want to Ignore.”

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.

Submission for assessment and placement on the IEEE registry can help app developers streamline their path toward regulatory review by demonstrating readiness against established clinical quality, safety, technical, and ethical criteria.

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.