Most people don’t think about their heart rhythm until something goes wrong. And by then, the moment has often passed, unrecorded and unexamined. Ultrahuman is trying to close that gap with the launch of Heart Scan, a new feature that lets Ring Air and Ring Pro users check their heart rhythm on demand, any time they feel something isn’t quite right.

What Heart Scan does and how it works

The feature is built on a partnership with FibriCheck, a CE-marked app that uses a smartphone’s camera to capture photoplethysmography (PPG) data and screen for irregular heart rhythms. The process is simple: open the Ultrahuman app, hold still for 60 seconds, and let FibriCheck’s algorithm do the analysis. The results are compiled into a PDF that users can share directly with a clinician, giving the healthcare provider access to the actual rhythm recording rather than a vague summary of symptoms.

That last detail matters. Self-reported symptoms are notoriously unreliable. A structured, shareable output that a cardiologist or GP can actually review is a meaningful step up from a patient saying “I felt a bit funny earlier.”

How it fits into Ultrahuman’s existing AFib tools

Heart Scan isn’t a standalone product. It extends Ultrahuman’s existing Atrial Fibrillation monitoring, which passively tracks heart rhythm during sleep. The on-demand scan adds a daytime, symptom-triggered layer to that continuous monitoring. So instead of relying only on overnight data, users now have a way to capture what’s happening in real time when something feels off.

And for existing subscribers, there’s no additional cost. Heart Scan is being folded into the existing AFib Power Plug add-on. If you’re already paying for AFib monitoring, you get this too.

Availability and current limitations

The feature is rolling out now, but not globally. It is currently limited to supported markets across the UK, Europe, the Middle East, and Asia-Pacific. Users in the United States will need to wait, and Ultrahuman hasn’t confirmed a timeline for broader availability.

That’s a real limitation. AFib affects an estimated 6 million people in the US alone, and consumer demand for accessible cardiac monitoring tools is high. The regulatory path for cardiac software features in the US is more complex, which likely explains the phased rollout, but it means American Ring users are sitting this one out for now.

Why this kind of tool has real clinical stakes

AFib is the most common sustained cardiac arrhythmia in adults, and a leading cause of stroke. The problem is that it’s often paroxysmal, meaning it comes and goes. Patients may experience symptoms briefly and then feel completely normal, making it easy to dismiss or miss entirely in a standard clinical setting. Wearable monitoring that can capture a rhythm at the moment of symptoms has genuine diagnostic value.

• On-demand PPG-based rhythm scan triggered by the user
• 60-second recording analyzed by FibriCheck’s algorithm
• PDF output formatted for clinical review and sharing
• Integrated with existing sleep-based AFib monitoring
• Included in the AFib Power Plug add-on at no extra cost

Still, it’s worth being clear about what this is and what it isn’t. A PPG-based scan from a smartphone camera is a screening tool, not a diagnostic one. It can flag potential irregularities, but it does not replace a 12-lead ECG or Holter monitor. Clinicians will need to interpret results in context, and users should be guided not to self-diagnose based on the output.

The bigger picture

Consumer cardiac monitoring is maturing quickly. Apple, Samsung, Withings, and now Ultrahuman are all building rhythm-detection capabilities into everyday devices. The shift toward continuous and on-demand monitoring outside of clinical settings is real, and the data quality is improving. The challenge ahead is integration: getting this data into clinical workflows in a way that is useful, not overwhelming, for the providers on the receiving end. A clean, shareable PDF is a start. But as these tools scale, the healthcare system will need clearer protocols for what to do when patients arrive with a folder full of wearable rhythm data. That conversation is only just beginning.

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Employer-sponsored weight management has a well-known problem: enrollment spikes, then engagement collapses. Weight Watchers and Google Health are betting that real-time wearable data can change that pattern. The two companies announced a collaboration on September 29, 2026, pairing Google Fitbit Air and Google Health Enterprise with Weight Watchers’ clinically supported weight health program, starting with select employer and payer clients.

What the partnership actually does

The integration gives eligible Weight Watchers members access to Fitbit Air, Google’s latest wearable, alongside the Google Health Enterprise platform. The idea is straightforward: daily health behaviors tracked by the device, things like activity, sleep, and heart rate, feed into the Weight Watchers program to give members and coaches a richer picture of what’s happening between weigh-ins. Google Health Enterprise supplies the AI-powered insights layer that connects those data streams.

For employers and health plans, this is packaged as part of Weight Watchers for Business. They don’t need to build separate integrations or run parallel vendor relationships. The benefit sits in one place, with wearable data reinforcing the behavioral and nutritional programming Weight Watchers already delivers.

Why employers are paying attention

Obesity-related healthcare spending is a real and growing line item for self-insured employers and commercial health plans. Weight management benefits have expanded significantly over the past few years, partly driven by GLP-1 drug coverage decisions and partly by pressure to show that behavioral programs produce measurable results. But uptake and sustained engagement remain stubborn problems.

Connecting passive data from a wearable to an active program is one credible response to that. Members who see their own data reflected in their program experience tend to stay engaged longer. And employers who can track aggregate engagement metrics have something concrete to bring back to benefits committees when justifying the spend.

Still, the evidence base for wearable-integrated weight programs is mixed. Device data can improve short-term engagement, but whether it translates to better clinical outcomes over 12 or 24 months is a question this kind of commercial rollout won’t answer on its own. What it does do is give Weight Watchers a meaningful technology differentiator at a time when the weight management market is crowded and shifting fast.

Key features of the collaboration

• Access to Google Fitbit Air for eligible Weight Watchers for Business members
• Integration with Google Health Enterprise for AI-powered health insights
• Daily behavioral data, including activity, sleep, and biometrics, connected to Weight Watchers programming
• Available through select employer and payer benefit programs
• Designed to improve member activation and long-term engagement

The broader context for health tech

This deal reflects a pattern that has been building for a while. Enterprise health platforms are increasingly being bundled with wearable hardware, moving away from consumer-facing device sales toward B2B distribution through employers and insurers. Google Health has made no secret of its ambitions in the enterprise space, and partnerships like this one are part of how it builds clinical relevance beyond the consumer market.

For Weight Watchers, the stakes are significant. The company has been repositioning itself aggressively as a science-backed health platform rather than a diet program, and aligning with Google Health adds credibility to that story. It also opens a distribution channel that doesn’t depend on consumer marketing, which matters given how competitive and expensive that environment has become.

Pricing and availability specifics were not disclosed beyond the current rollout through select Weight Watchers for Business clients. Broader availability across employer and payer programs is expected to expand from there. Clinicians advising patients on employer benefits should watch for this option appearing in health plan portals over the coming year.

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When the FDA recently told a pharmaceutical company it could not proceed to clinical trials because its animal data wasn’t enough, and that organ-on-a-chip data was needed instead, it marked something quietly extraordinary. Fifteen years ago, the same agency’s peer reviewers were asking researchers to add mouse experiments to validate their early chip-based models. The standards have reversed direction entirely.

That reversal is the backdrop for one of the more interesting tensions in biomedical research right now. The technology to model human biology without animals has advanced well past proof-of-concept. What’s lagging is everything else: the regulatory frameworks, the training pipelines, the professional habits built up over decades of animal-based science.

What organs-on-a-chip actually do

The story starts in 2010, when cell biologist Donald Ingber and colleagues at Harvard’s Wyss Institute published a paper in Science describing a model human lung smaller than a USB stick. The device used narrow microfluidic channels lined with human lung and blood vessel cells, and it physically expanded and contracted when air was pumped through adjacent chambers. It breathed. When exposed to bacteria, the model mounted an inflammatory response. When exposed to silica nanoparticles, it revealed that physical movement affected how lung tissue absorbed them. Static tissue cultures had never been able to show that. The paper has since been cited by nearly 5,400 other studies.

That lung chip was an early example of what researchers now call NAMs, or new approach methodologies. The category includes organoids, computational organ simulations, AI-assisted analysis tools, and multi-organ chip systems that link up to ten organ models together to approximate human physiology. None of these systems are perfect. But the argument made by their developers is not perfection — it’s that they already outperform animal models on many of the questions that matter most in drug development.

Why animal models have real limitations

Around 92 percent of drugs that enter U.S. clinical trials never reach the market. Many fail because they prove ineffective or unsafe in humans in ways animal testing didn’t predict. Failure rates are especially high in cardiovascular disease, oncology, and neurology. This isn’t entirely the fault of animal models — flawed study design and the sheer complexity of disease also play a role. But the pattern is consistent enough that researchers have long questioned whether mice and monkeys are adequate surrogates for human biology.

Some go further. There’s a plausible case, made by more than a few scientists, that drugs like aspirin or acetaminophen might have been abandoned during preclinical testing if they were discovered today, because of how they behave in certain animal models.

Regulation finally opened the door

For years, U.S. drug regulations effectively mandated animal testing before human trials. That changed in late 2022, when the FDA Modernization Act 2.0 became law. It explicitly authorized NAMs as valid evidence in preclinical drug studies. For researchers who had spent years building the scientific case for alternatives, it was a long-awaited shift.

But legal authorization is not the same as adoption. As Ilka Maschmeyer, a translational toxicology researcher and executive at German biotech company TissUse, puts it, cases where companies are actively required to use NAMs are still rare. The expectation is that they’ll become more common. Still, that process has a long way to go.

The real obstacle is change management

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

Validating NAMs at scale requires standardizing systems across labs, running head-to-head comparisons with animal experiments, and building enough of an evidence base that regulators and pharmaceutical companies feel confident relying on them. That takes time and coordinated effort. And it requires researchers trained in these methods — which means rethinking curricula in biomedical and pharmacology programs that have long centered on animal models.

• Standardization of NAM protocols across research institutions
• Regulatory guidance on what counts as sufficient NAM evidence
• Training programs for researchers and toxicologists
• Head-to-head validation studies comparing NAMs to animal models
• Cultural shifts within pharmaceutical R&D departments

The technology is not what’s standing in the way. That’s an important thing to understand about where this field actually is. The chips work. The organoids work. The computational models are getting better every year. What hasn’t caught up is the institutional infrastructure needed to make their use routine. That gap is closable, but it won’t close by itself.

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About 85% of breast cancers occur in women with no family history of the disease. That single statistic is the clearest argument for why risk assessment tools need to go beyond family history, and it’s the clinical rationale behind a new partnership that could reach millions of women who currently have no reason to think they’re at risk.

Everlywell announced on October 1 the nationwide commercial launch of Clairity Breast, an FDA-authorized AI software that estimates a woman’s five-year risk of developing breast cancer using images from a standard screening mammogram. The product is now available through Everlywell’s consumer platform, with ordering and review handled by licensed providers in Everlywell’s affiliated clinician network.

What Clairity Breast actually does

Clairity Breast does not screen for cancer or detect existing tumors. It analyzes patterns in mammogram images, patterns that are not visible to radiologists, to estimate the probability that a woman will develop breast cancer within the next five years. The output gives women and their clinicians a new data point for planning screening frequency and risk-reduction strategies.

The software received FDA De Novo authorization in May 2025, making it the first AI-based tool cleared for this specific purpose. It has since been included in the NCCN Clinical Practice Guidelines in Oncology for Breast Cancer Screening and Diagnosis. Updated NCCN guidelines published in 2026 now recognize AI-based mammogram risk assessment as an option for identifying women at increased risk, starting at age 35. Prior to this Everlywell partnership, access was limited to a small number of health systems including Beth Israel Deaconess Medical Center in Massachusetts and Invision Sally Jobe in Colorado.

How the Everlywell platform delivers it

Women with a qualifying screening mammogram from the past 12 months can access the assessment through the Everlywell platform. The process includes review and ordering by a licensed provider, followed by results released with next steps based on national guidelines. Women can then share their results directly with their own physician to inform a personalized screening or risk-reduction plan. Everlywell’s clinical team is also available to answer questions about individual results.

Clairity Breast joins a growing portfolio of cancer screening products on the platform, which already includes at-home colon cancer screening, an HPV self-collection kit for cervical cancer screening, and access to multi-cancer early detection testing.

The clinical case for broader access

The value of a tool like this sits in the gap between current practice and what’s possible. Standard risk assessment relies heavily on family history and genetic markers like BRCA1 and BRCA2. But most women who develop breast cancer don’t carry those markers and have no affected relatives. Their mammograms are read for findings today, not for risk tomorrow.

Clairity Breast was validated across more than 77,000 screening mammograms at five U.S. sites for FDA authorization, and has since been validated in more than 120,000 mammograms across 10 U.S. facilities. That’s a meaningful evidence base for a relatively new category of tool.

“The information we need to understand a woman’s breast cancer risk has been in her mammogram all along, we just haven’t been able to interpret it until Clairity Breast,” said Dr. Connie Lehman, founder and CEO of Clairity and professor of radiology at Harvard Medical School.

Why this partnership matters beyond the product

The Everlywell and Clairity partnership also reflects a broader shift in how specialized clinical tools reach patients. Health system rollouts are slow. Consumer platforms with existing clinical infrastructure can move faster, and Everlywell has the reach to make that count. The company reports delivering nearly one billion personalized health insights and serving more than 60 million people since its founding.

Both companies are female-founded and female-led, a detail worth noting in a space where women’s health has historically been underfunded and under-researched. Julia Cheek built Everlywell; Dr. Lehman, a radiologist and researcher, built Clairity. The product they’ve brought to market together addresses a real gap in preventive care, and it does so using infrastructure that already exists in every qualifying mammogram a woman gets.

Pricing details were not disclosed at launch. The assessment is available now at everlywell.com.

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For the first time in the United States, a state has cleared a software system to issue prescriptions without a physician approving each one beforehand. The condition being treated is acne. The implications, though, reach well beyond dermatology.

Utah has authorized New York-based health technology company Nolla Health to run a controlled pilot through which its AI system can select and issue initial acne prescriptions autonomously for eligible adults. The program operates through the Nolla Derm app and is available to Utah residents aged 18 and older. Patients complete a 10 to 15 minute intake questionnaire and a facial scan. The system then assesses their skin and selects a treatment from a predefined list of physician-approved topical options. According to Nolla, the whole process can produce a prescription within minutes.

How the autonomy is introduced — carefully

The rollout is deliberately staged. For the first 100 patients, a physician approves every prescription before it goes out. In the next phase, covering up to 500 patients, the system prescribes autonomously but a physician reviews all cases retrospectively at the end of each day. In the third phase, doctors review a sample of prescriptions once a week. So this is not a sudden hand-off to an algorithm. It is a structured transfer of authority with built-in clinical oversight at each step.

The system also has hard limits. It cannot improvise or choose freely among available drugs. It can only select from a short list of approved topical treatments and predefined pathways. And when a patient falls outside those parameters, the system stops and routes the case to a licensed physician. That constraint matters. It means the AI is not making open-ended clinical judgments — it is executing decisions within a tightly bounded space that clinicians have already defined.

The technology behind the decisions

Nolla says its platform uses multimodal machine-learning, including vision transformers, to analyze facial images alongside structured patient data from the intake form. The company reports its broader systems have been trained on more than 3 million labeled clinical cases. For the acne pilot specifically, Nolla says licensed clinicians agree with the system’s treatment choices in more than 96 percent of real-world cases. Disagreements, the company says, have typically involved minor adjustments like changing a topical medication’s strength rather than selecting a fundamentally different treatment. That figure was provided by Nolla and has not been independently verified.

Why this is different from standard telehealth

Most digital health platforms collect symptoms or assist clinicians, but a human still makes the final call. That’s the conventional model. Nolla’s system is designed to eventually make that call itself, within limits. The distinction is significant both clinically and regulatorily. The pilot was developed with Utah’s Office of Artificial Intelligence Policy under the state’s framework for testing new AI applications, which gives it a formal regulatory structure rather than operating in a gray area.

What this means for patients and clinicians

Patients who receive a prescription through the app can choose home delivery or pharmacy pickup. The app also uses daily skin scans to monitor progress, with treatment plans reviewed monthly based on response. Direct access to a licensed physician is available when needed. The service starts at $4.99 per month under the Utah pilot. Nolla says the app has been downloaded more than 175,000 times across 44 states, though autonomous prescribing is currently only available to eligible adults in Utah.

For clinicians, the pilot raises questions worth tracking closely. Acne is a low-risk condition with well-established treatment protocols, which makes it a reasonable place to test this model. But the infrastructure being built here, algorithmic prescribing with retrospective physician review, could be proposed for other conditions over time. Whether the clinical safeguards scale appropriately will depend heavily on the data that emerges from this pilot. That data, and who gets to see it, will matter enormously.

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Price transparency in American health care has produced an enormous amount of data and surprisingly little clarity for patients. Hospitals publish machine-readable files of their charges. Imaging centers and clinics post self-pay prices on their websites. Yet a patient paying cash for a scan still has to piece together a comparison from scattered sources, then figure out how to hold a provider to the number they found.

BoDirect is a consumer tool built to close that gap. It does three things: it compares posted self-pay prices near you, it helps you get your chosen provider’s price in writing, and it helps you check the bill against that written estimate.

Search and compare

The starting point is a search by service and ZIP code. BoDirect lines up the self-pay prices that providers post for that service nearby, each labeled with its source, such as a provider’s website or a hospital’s price file, and the date it was checked. The result is a side-by-side view that makes price variation easy to see. In the example on its site, a knee MRI without contrast carries posted prices ranging from $410 at one imaging center to $1,900 at a hospital, all within the same area.

The tool is careful about what a posted price means. It treats it as a starting point and makes clear that the provider’s written estimate is what counts.

Save and request

Once a patient picks a provider, BoDirect helps them ask for a Good Faith Estimate, the written estimate that uninsured and self-pay patients are entitled to request under federal rules. The Plus plan drafts the request letter automatically from the service and provider saved, including the posted price the patient found, and asks the billing office to confirm the charges for the service and any related reading or facility fee. When the letter is ready, BoDirect sends a text notification to the account holder. The four-step process is laid out as search, save, get it in writing, and check the bill.

Each saved service lives in a folder alongside its posted price and, once it arrives, the provider’s written estimate. The free plan keeps two services. Plus keeps as many as a patient needs, for $7 a month or $59 a year.

Check the bill

The final piece is the one many price tools skip. When the bill arrives, BoDirect’s bill check compares it to the provider’s written estimate, one provider or facility at a time. If the billed amount is $400 or more above that provider’s estimate, the tool flags that the patient may be able to start the federal patient-provider dispute process, which generally must begin within 120 calendar days of the bill date.

That threshold and timeline come from the No Surprises Act. BoDirect presents them as general information, not legal advice, and notes that it is not a law firm.

Why the paper trail matters

For self-pay patients, the value is less about finding the lowest number and more about creating a record. A posted price found online, a written estimate from the chosen provider, and the final bill, all filed together, give a patient something concrete to point to if the charges change. Without that record, disputing a bill is mostly a matter of memory and phone calls.

BoDirect’s self-pay price tool packages that record into a single workflow. For a growing number of patients on high-deductible plans or without coverage, that kind of structure is becoming a practical necessity rather than a convenience.

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A battery that dissolves inside you sounds like a problem. MIT researchers are betting it’s actually the solution. Their new bioresorbable battery, published in Nature Chemical Engineering, powers ingestible electronics for several days before breaking down into compounds the body can safely absorb. It’s a meaningful step forward for a field that has long struggled with a basic tension: the most useful ingestible devices often need electricity, and electricity usually means materials you don’t want left inside someone.

Why powering ingestible devices has always been complicated

Swallowable electronics have been in development for years. MIT’s own research group has previously built capsules capable of tracking vital signs, delivering drugs, and detecting opioid overdoses. But keeping those devices powered is its own engineering challenge. Some designs harvest energy from the gastrointestinal tract. Others use conventional batteries, which work well enough until their casing is compromised, at which point their contents become a safety concern. So the question isn’t whether ingestible electronics can be useful. It’s whether they can be powered safely.

What the battery is made of, and how long it lasts

The new design uses magnesium as the anode and molybdenum trioxide as the cathode, both materials considered tolerable in small quantities. An ionic liquid gel acts as the electrolyte. Together, they produce 1.84 volts. In a solution mimicking stomach acid, the batteries performed normally for around three days. Output then declined gradually, with full breakdown occurring within a few weeks. That timeline is actually well-matched to most diagnostic or therapeutic applications, where a device only needs to function for a short, defined window.

Stimulating hunger hormones and tracking medication

The team tested the battery in two animal experiments. The first used a rectangular battery inside a device that applies gentle electrical stimulation to the stomach lining. Prior research had shown this stimulation prompts stomach cells to release ghrelin, the hormone associated with hunger. After just 20 minutes of stimulation, ghrelin levels rose by roughly 50 percent. That kind of effect could eventually benefit patients dealing with nausea or appetite loss, though human trials are still ahead.

The second test used a disc-shaped battery to power an RFID tag built into a medication-tracking capsule. In animals, it maintained continuous signal transmission from inside the digestive tract and extended communication range. The researchers plan to begin a clinical trial for this system, called SAFARI, within about two years.

The broader case for materials that disappear

Beyond patient safety, there’s an environmental argument here too. Ingestible devices that pass through the body and enter sewage systems carry their own downstream risks. Batteries built from degradable materials reduce that burden.

• Bioresorbable design eliminates the need for device retrieval
• Materials are considered safe in small physiological amounts
• Three-day functional window suits most short-term clinical applications
• Environmental impact is lower than conventional battery materials entering wastewater

Still, a lot of work remains before any of this reaches patients. Animal data is promising, but translating gastrointestinal electronics into clinical practice involves regulatory, manufacturing, and safety hurdles that take years to clear. What the MIT team has shown is that the chemistry is viable. That’s not a small thing. In a field where the battery is often the weakest link, building one that’s designed to disappear might be exactly the right starting point.

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Multi-site stereotactic body radiotherapy in as little as 60 seconds. That’s the headline claim behind Accela, the new radiotherapy system Siemens Healthineers launched this week through its cancer-care division, Varian. If that number holds up in broad clinical use, it represents a meaningful shift in how radiotherapy departments manage throughput, patient comfort, and treatment complexity.

What Accela actually is

Accela is a linear accelerator built around NeoArc, a new hardware architecture that Siemens Healthineers describes as the foundation of the entire system. The machine is designed to handle a wide range of cases while making complex treatments faster and more accessible for centers that may not have the staffing or scheduling capacity to run long treatment sessions.

The technical specifications are notable. Accela includes a large HyperSight cone-beam CT imaging panel, a newly designed multi-leaf collimator, gantry rotation at 2.5 RPM, dose rates up to 40 Gy/min, and a new eight-megavolt flattening filter-free energy setting. The planning software uses AI and advanced algorithms to balance plan quality against delivery speed, which has historically been a difficult trade-off for clinical teams to manage manually.

Why speed matters here

Stereotactic treatments require patients to hold extremely still, often while holding their breath, so the tumor target doesn’t shift during irradiation. Shorter delivery times reduce the chance of movement, reduce patient distress, and make it practically possible to treat multiple sites in a single session. For patients with metastatic disease, that matters a great deal.

Dee Khuntia, chief medical officer at Varian, framed the significance this way: “It’s faster than previous systems, it’s built on a fundamentally modern architecture, and it introduces a generational shift in treatment planning. But what excites me most is its potential to alter the biological response to radiotherapy. That’s not incremental progress; it’s a new chapter.” The biological angle is worth watching. Ultra-high dose rates, sometimes called FLASH radiotherapy, have shown promise in preclinical work for sparing healthy tissue. Whether Accela’s dose rates translate to that kind of biological benefit in patients is a question ongoing research will need to answer.

The clinical input behind it

The system wasn’t developed in isolation. Siemens Healthineers worked with an advisory group of seven major cancer institutions over the course of eight years:

• Mayo Clinic
• Washington University School of Medicine Siteman Cancer Center
• University Hospital Zurich
• Seidman Cancer Center at University Hospitals Cleveland Medical Center
• Maastro, Maastricht Radiation Oncology Institute
• Mass General Brigham Cancer Center
• Memorial Sloan Kettering Cancer Center

That depth of clinical collaboration during development, rather than after product launch, is genuinely unusual. It suggests the engineering decisions were stress-tested against real workflow constraints and patient scenarios, not just optimized for marketing specifications.

The broader context for radiation oncology

Cancer incidence is rising globally. Radiation oncology departments are under pressure from higher caseloads, more complex treatment regimens, and persistent workforce shortages. A system that can treat more patients per day, with less time per session and without sacrificing precision, addresses a real operational problem. So the clinical case for faster, high-dose-rate linac technology is not theoretical. The question for procurement teams will be whether Accela’s performance in routine clinical settings matches what its launch specifications promise.

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Around 115 million American adults have prediabetes, and roughly 80 percent of them have no idea. That statistic has driven a lot of product development in metabolic health tech over the past few years, but most of it has stopped at the data layer. Abbott and Berry Street are now trying to close that gap.

The two companies announced an exclusive partnership on September 23, 2026, combining Abbott’s Lingo continuous glucose monitor with Berry Street’s network of registered dietitians. The idea is straightforward: glucose data tells you what’s happening in your body, but a trained clinician helps you figure out what to do about it.

What Lingo does, and who it’s for

Lingo is an over-the-counter CGM designed for adults 18 and older who are not using insulin. It tracks how food, exercise, sleep, and stress affect glucose levels in real time, and feeds that information into a companion app. It is not a diagnostic device and is not intended for managing diabetes. Think of it as a metabolic awareness tool for people who want to understand their bodies before a clinical problem develops.

It’s available at hellolingo.com, Amazon, Walmart, Walgreens, Best Buy, and Publix, and works with both Android and iOS devices.

What the partnership actually offers

Through the new arrangement, Lingo and Berry Street users get access to a more integrated experience. Specifically:

• Registered dietitians trained specifically on Lingo and CGM data interpretation
• Personalized nutrition recommendations tied to an individual’s glucose trends
• Virtual one-to-one appointments, accessible anywhere in the United States
• Berry Street users can purchase Lingo at a 15 percent discount on four-week plans
• Lingo users are directed to Berry Street sign-up through the Lingo website and email

Berry Street is in-network with more than 1,250 health plans, and the company says 96 percent of its clients pay nothing out of pocket. Coverage still depends on individual plan details, so users will need to verify their own benefits.

The clinical case for combining data with human expertise

This is not just a marketing pairing. Research published in the American Journal of Clinical Nutrition has shown that registered dietitian-led nutrition therapy can improve glucose control, weight, cholesterol, and other cardiometabolic markers, including in people with prediabetes. The data is solid enough that this kind of medical nutrition therapy is increasingly covered by insurers, which is precisely what Berry Street has built its business around.

But published evidence on CGM use in non-diabetic populations is still maturing. What we know is that glucose variability matters even below diabetic thresholds, and that personalized feedback can influence behavior more effectively than generic dietary advice. The combination of continuous monitoring and clinician interpretation is a reasonable clinical hypothesis. Real-world outcomes from this partnership will be worth watching.

Why this matters beyond the product announcement

The metabolic health space has been crowded with wearables and apps promising insight without much follow-through. What makes this partnership worth attention is the insurance coverage angle. Berry Street’s ability to bill more than 1,250 health plans substantially lowers the access barrier for dietitian support, and that changes the economics of the whole offering.

For clinicians, it also raises a practical question worth considering: patients who show up with months of CGM data and a dietitian-informed nutrition plan are going to need primary care and specialist teams who know how to work with that information. The tools are getting ahead of clinical workflows in some settings, and that’s a gap worth preparing for.

So far, Lingo is available in the US and the UK. Whether Abbott expands this partnership model to other markets, or builds similar arrangements with other care providers, will say a lot about where the company sees the CGM category heading for the general wellness population.

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Importing treatment data in radiation oncology can require up to 17 manual steps. That single fact says a lot about why cancer patients sometimes wait weeks before treatment begins. GE HealthCare and Mass General Brigham are now working together to see whether generative AI can do something about it.

The collaboration, announced September 23, 2026, will explore building a generative AI tool designed to sit inside GE HealthCare’s Intelligent Radiation Therapy (iRT) platform. The goal is straightforward: give radiation oncology teams a way to query structured and unstructured clinical data, including notes, images, and treatment plans, from a single interface, rather than hunting across disconnected systems.

Why radiation oncology is a data problem as much as a clinical one

Nearly 60% of cancer patients receive radiation therapy at some point in their care. But the planning process behind that treatment is fragmented by design, or rather by historical accident. Multiple care teams, multiple systems, and a heavy reliance on unstructured information like free-text clinical notes mean that pulling together a coherent picture of a patient’s treatment journey takes real time and real effort.

One study of more than 500 radiation oncology cases found over 44 variations in treatment-planning workflows. That kind of variability isn’t just inefficient. It creates conditions where information gets missed and decisions get delayed. The proposed AI tool would let clinicians ask plain-language questions to surface relevant patient information quickly, with the system returning a summarized view to support more informed, individualized treatment decisions.

Built on a proven workflow foundation

This isn’t a cold start. The iRT platform itself grew out of earlier work between GE HealthCare and Massachusetts General Hospital, a founding member of the Mass General Brigham system. Software developed at MGH became a core component of iRT, and the results were significant. Analysis of more than 11,000 treatment plans at MGH showed the technology helped cut the time from patient intake to treatment start from as long as 30 days down to eight.

John Wolfgang, PhD, a clinical physicist in the Department of Radiation Oncology at Mass General Brigham and a principal investigator on the new research, described the ambition clearly: ‘We are excited to advance this work to explore how AI could further support radiation oncology care teams by making complex treatment information easier to access, understand, and act on.’

What the research will and won’t tell us

It’s worth being precise about what this announcement actually is. This is a research collaboration exploring whether a generative AI tool could work in this context. No clinical product has been announced. The findings are expected to inform future iRT capabilities across MR, CT, and theranostics workflows, but the timeline and regulatory pathway for any resulting tool remain open questions.

Still, the direction is clear, and it reflects a broader trend in health AI: moving from diagnostic support toward workflow intelligence. The hard part in radiation oncology has never been a shortage of data. It’s that the data lives in the wrong places, in the wrong formats, and takes too long to synthesize under clinical pressure.

The bigger picture for AI in cancer care delivery

Hospitals in multiple countries are already using iRT to manage radiation therapy and theranostics workflows. GE HealthCare’s Sam Kandala, General Manager of Therapy Guidance, framed the new work as a natural extension: ‘This new collaboration gives us an opportunity to build on that foundation and explore how AI could help clinicians not only work more efficiently but also make better use of the information available to them as they personalize treatment for each patient.’

If the research produces what the teams are hoping for, the clinical benefit isn’t just faster workflows. It’s clinicians spending less time searching for information and more time acting on it. For cancer patients already facing a long road, that shift in where attention goes could matter considerably.

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