That question makes AI in Health: A Guide to Winning in the New Age of Intelligent Health more than a book about technology. It is a leadership guide designed to make people think long and hard about using AI in ways that improve care, support caregivers and remain accountable to the people health systems exist to serve. Lawry understands the technology, yet throughout the book he brings readers back to something more consequential: what these increasingly powerful tools mean for people.

A former Microsoft leader in health and life sciences, health-system executive, founder of health technology companies and now principal consulting firm Second Century Tech, Tom Lawry helped chart this field before health AI became a fixture of every conference agenda. That experience gives this second edition unusual authority. He understands how quickly the technology is changing while keeping his attention on the people who must live with its consequences.

Lawry Combines Leadership with Caring

In an endorsement appearing on the book’s back cover, I wrote: “Tom Lawry understands that AI has no purpose apart from humanity, making this book an indispensable roadmap for leaders who want innovation to amplify compassion, judgment and the human experience of care.” Having reviewed this new edition, I continue to believe that assessment is essential and belongs on the desk of anyone involved in health AI or seeking a state-of-the-art perspective.

Lawry is, without doubt, a fan of AI applications.  He recognizes the risks and responsibilities.  He also reflects on the struggle of the nation’s sick care system and sees that “AI has the ability to save health care from itself.”

Early in AI in Health, Lawry asks readers to consider two photographs of the same New York intersection, twenty years apart. In 1905, horses filled the streets around the Flatiron Building. By 1925, automobiles had replaced them. The buildings remain; the world around them has changed.

Lawry’s point is not simply that technology advances. Transformative technologies change how people prioritize workflow, how organizations operate and what society comes to expect, often before those living through the change fully recognize what is happening. Health care, he suggests, is standing at this intersection now.

That makes leadership the book’s essential subject. AI is no longer something health leaders can simply observe or delegate to the technology team. Lawry asks them to decide what problems it should solve, what values should guide its use, how the workforce should be engaged and who remains accountable when intelligent systems influence care.

From a Promising Pilot to Better Care

One of the edition’s strongest chapters concerns responsible AI. Lawry presents a hospital scenario in which an adverse-event prediction model yields an impressive overall improvement. However, a closer look reveals it performs far better for one patient population than another. The average result looks successful. Beneath it lies a more consequential question: Who benefits, who may be left behind and who is accountable for the difference? That is the kind of question every health leader should be prepared to ask before celebrating a successful pilot.

Lawry carries the point into governance: What is a tool designed to do, and for whom? What evidence supports its effectiveness? How does it perform across populations? What happens when it is wrong? Who owns the strategy, risk, monitoring and response? These are operational decisions with moral weight. A model can meet a technical target and still fail the people who need it most.

Separately, Hal Wolf, President and CEO of HIMSS, the world’s largest health technology professional society and a center for global conversation on technology and health, has repeatedly emphasized that realizing AI’s promise requires leadership and governance. It is fitting that Lawry’s book will once again find itself on the shelves of the HIMSS Global Health Conference bookstore.

The book also earns its place as a working guide. Lawry moves from the principles of leadership and governance to practical exercises for building an AI leadership charter, setting a vision, engaging the workforce, selecting use cases and assessing vendors. These sections give teams a way to turn an ambitious conversation into decisions they can revisit and measure. The chapter on why promising AI projects fail to scale is especially useful for organizations surrounded by pilots but unsure which efforts deserve lasting support.

Readers who already know the technical vocabulary may find the AI primer familiar. They should keep reading. Its value lies in connecting that vocabulary to leadership choices, including when to automate a task, when to support professional judgment and when to leave a decision firmly with a person. Those distinctions matter greatly in health, where a confident answer can be mistaken for a correct one and a convenient workflow can conceal an inequitable result.

Kathleen McGrow, DNP, MS, RN, PMP, FHIMSS, FAAN, Global Chief Nursing Innovation Officer at Microsoft Health & Life Sciences, calls this edition “…both a strategic guide and a leadership manifesto for a healthcare system at a critical inflection point.” Her back cover assessment fits a book that treats workforce experience as central to AI strategy. Lawry asks leaders to involve the people delivering care in deciding where intelligent tools can genuinely help.

From Applications to Expectations

Lawry’s scope is broad, reaching from consumer expectations and clinical applications to workforce development and the future of medicine. The breadth makes the book relevant beyond hospital leadership. Developers, researchers, payers, communicators and policymakers all shape the conditions under which AI reaches people. Each has reason to ask whether a claimed improvement is visible in a patient’s life or a caregiver’s day.

AI technologies will continue to change at extraordinary speed. Lawry wisely builds this edition around leadership questions that should outlast any individual tool or product announcement. What problem are we solving? What evidence will tell us whether people benefit? Whose experience is missing from the design? How will we respond when the evidence changes? A leader who can answer those questions is better equipped for both the next generation of tools and the current one.

That is why this second edition belongs on the desk of anyone helping shape health AI. Lawry writes as a pathfinder who understands the technology while never losing sight of the people expected to benefit from it. His perspective draws not only from his years inside health systems and technology but also from his continuing work as a global advisor and, perhaps most importantly, as a constant observer.

He listens, questions and learns from the people confronting these changes in real time, bringing to AI in Health a perspective shaped by experience, curiosity and an enduring desire to understand what comes next. At a moment when intelligent tools can move faster than health systems can absorb them, AI in Health makes the essential case that greater intelligence becomes better care only when people are prepared to lead.

Available Now on Amazon

AI in Health is now available for pre-order on Amazon.

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No one asked what he was taking. No one asked why he stood sideways in the bathroom mirror every morning, pulling up his shirt and frowning. And no one noticed that his heart rate was lower than it should have been for a boy his age.

Dalton is a composite, drawn from the stories of many families. But what happened to him, and what didn’t happen, is real. It’s happening in homes across the country.

It Was Never Only a Girls’ Problem

For decades, when we talked about body image, eating disorders, and the harm done by images in the media, we talked about girls. Girls were the ones being told to be thinner, and girls were the ones being admitted to hospitals. Parents, physicians, and schools learned to watch girls for trouble.

Boys were left out of that conversation, and they’re still being hurt by it.

In Ontario, Canada, researchers studied more than 11,600 hospital stays for eating disorders in children and teens between 2002 and 2019. For boys, hospital stays rose by more than 400%. For every boy sick enough to need a hospital bed, many more are struggling where no one sees it.

Part of the reason is the way we look for trouble. In 2020, researchers who reviewed what’s known about eating disorders in boys and young men found that the screening tools physicians use were mostly designed with girls in mind. They ask about wanting to be thinner, not about wanting to be bigger. A boy with a serious problem can answer every question the “right” way and never be flagged. The same review found that treatment programs were also built mostly for girls, and boys may do worse in them as a result.

That’s the main problem. Boys’ body worries usually center on muscle, not thinness. So a boy in real danger can look like a boy in great shape.

What Boys Learn, and What They Buy

Boys learn early what kind of body earns respect. They see it in superhero movies, in older boys at school, and in grown men joking about getting “shredded.” They learn that this body is what they should work toward, even if it costs them their health. And now they see that message on their phones every day.

In 2025, Common Sense Media surveyed more than 1,000 boys ages 11 to 17 across the United States. Nine out of ten said they see messages about body image online. About two out of three of the boys who saw content about how men should look and act said it showed up in their feeds without them searching for it. About 1 in 4 said social media makes them feel they should change how they look.

Boys don’t just watch. They act. A University of Minnesota study of nearly 2,800 middle and high school students found that more than 90% of the boys had exercised more in the past year to build muscle. More than one-third had used protein powders or shakes, and nearly 6% said they had used steroids. These were ordinary kids, not top athletes.

The influencers boys follow often make money from what they promote. Many earn a share of every protein powder or fat burner sold with their discount codes. Some push far more dangerous products. In 2023, the Center for Countering Digital Hate found that TikTok videos promoting steroids and similar drugs under certain hashtags had been viewed up to 587 million times in the United States over three years. The group also found 35 influencers on the app linking to websites that sold these drugs.

The harm is real. Researchers at the University of Toronto found that more than 1 in 4 boys and young men in a national Canadian sample had symptoms of muscle dysmorphia strong enough to be of real concern. Muscle dysmorphia is a condition in which a young man believes he’s small and weak, even when he isn’t. Most boys who have it have never heard its name.

The products carry their own risks. A study published in the New England Journal of Medicine estimated that supplements send about 23,000 people in the United States to the emergency room each year, with weight-loss and energy products among the most common causes.

Why Parents and Physicians Miss the Signs

When a boy is in trouble with his body, his trouble often looks like success. A boy who works out every day seems disciplined. A boy who eats “clean” seems healthy. A boy who drinks protein shakes seems like every other kid on the team. Adults praise these habits. And boys are taught not to talk about their feelings, so a boy who feels ashamed keeps it to himself.

Physicians aren’t always asking, either. In 2025, a survey of more than 800 Canadian pediatric specialists found that 17% had seen at least one young patient in the past year who needed medical care because of a performance-enhancing supplement or drug.8 Most of those patients were boys, and almost a third were only 10 to 12 years old. Yet more than half of the physicians surveyed said they never asked their young patients about these products.

The stakes are high. In the United States, men die by suicide at about four times the rate of women. Boys and men are less likely than girls and women to ask for help.

So what can we do? Parents can learn the signs. Watch for a boy who panics when he misses a workout, trains through injuries, follows strict food rules, spends long periods checking his body in the mirror, skips family meals, or has powders and pills you don’t recognize. Talk with him about health and strength, not size. Ask what he’s watching and whom he trusts online. And make a simple family rule: before he takes anything meant to change his body, he talks to you, and ideally to his physician, first.

Physicians need training to look for danger in boys, not just girls. At every checkup, they can ask boys two simple questions: “What are you doing to change your body?” and “Are you taking anything to build muscle or lose fat?” They can compare heart rate, growth, and weight with earlier visits. And they can take a parent’s worry seriously, even when the boy in the exam room looks strong.

If you’re worried about a boy and feel brushed aside, it’s okay to ask for a second opinion or a referral to a specialist in adolescent medicine or eating disorders. If a boy you love is struggling or talks about hurting himself, call or text 988 to reach the 988 Suicide and Crisis Lifeline.

Boys aren’t less at risk than girls. They’ve simply been overlooked. It’s time we changed that.

I’ve written much more about this in my new book, “Built to Be Seen: How Influencers and Supplement Sellers Endanger Boys and Young Men.” It’s a short read, a 74-page paperback to be published shortly on Amazon, written in plain language for parents, coaches, teachers, and young men themselves. It explains how social media finds boys, what’s really in the supplement aisle, the dangers of steroids and online testosterone clinics, and the warning signs to watch for.

The post Boys Are Risking Their Health to Look Like Influencers, and Almost No One Is Checking appeared first on Medika Life.

The obvious question is whether that’s safe. But another question interests me more. I have spent 36 years taking care of people with cancer. By the time someone reaches my office, an enormous number of decisions have already happened. Someone noticed something. They decided whether it mattered. Perhaps they waited. Perhaps they searched online. Eventually, they decided to tell another human being. Medicine usually enters the story after that decision. AI is beginning to enter before it.

What happens before you arrive

When you wake up with an unfamiliar pain, the choices available to you have changed significantly. For most of human history they were limited: ignore it, ask someone you trusted, or seek medical care. Then the internet gave us another option, and millions began typing symptoms into search engines. Now AI alters that interaction in a way that matters. A search engine gives you pages to sort through. An AI system gives you a conversation. You can describe where something hurts, how long it has been happening, what makes it worse, what medications you take, what you are worried it might mean. You can ask another question, clarify an answer, ask again. It can feel remarkably close to consultation.

I don’t think the answer is to tell people to stop asking AI about their health. People have always sought medical information outside doctors’ offices, and AI can be genuinely useful for some things. It can explain an unfamiliar laboratory result in ordinary language. It can help someone understand the difference between two treatments. It can generate questions to take to an appointment or help make sense of something a physician said, something the patient only half-absorbed while frightened or tired. Medicine asks people to absorb complicated information precisely when they may be overwhelmed, and a tool that helps close that gap has real value.

But there is a threshold where explanation quietly becomes reassurance, and that’s where the problem begins. A symptom does not have to disappear for someone to delay seeking care. It only has to become sufficiently explainable. A persistent cough becomes allergies. Fatigue becomes stress. A change in bowel habits becomes diet. A new lump becomes something probably benign. And most of the time, the reassuring explanation may actually be right. Most headaches are not brain tumors. Most back pain is not metastatic cancer. Most strange sensations that appear during an ordinary week will never become dangerous. That is precisely why triage is difficult. The challenge is not simply identifying the most likely explanation. It is recognizing the uncommon situation in which the most likely explanation is wrong.

When I meet a patient, I am not simply processing the words they say. I am trying to understand the story around those words. How quickly did this change happen? Does the symptom fit the rest of their medical history? Is there something about their age, medications, previous illness, or family history that changes what an otherwise ordinary symptom means? Sometimes the most important information emerges only because I ask a question the patient did not know was relevant. But here is the vulnerability built into any conversation with an AI system: much of what it knows about you is what you decide to tell it. You may not know what matters. And a confident, coherent answer can feel more complete than the information behind it actually is.

For years, physicians worried about patients arriving with information they found on Google. I suspect we are entering a much more consequential era. Patients may increasingly arrive after having already had a long conversation about their symptoms — with possible explanations, questions they want to ask, interpretations of laboratory results, and some sense of how urgently they should seek care. Sometimes that will make the medical encounter better. The patient may be better informed, the questions sharper, medical language less intimidating. A person who might otherwise have ignored a warning sign could even be persuaded to make an appointment. But the opposite can happen too. The conversation may reassure someone enough that the medical encounter never happens. Once AI becomes the first opinion, it can influence whether there is ever a second.

I write about the signals we often miss in our health and our lives. If you’re curious about what your own body may be telling you, my free 3-minute Capacity Checkup can help you see where strain may already be showing up in sleep, mood, focus, and body.

But after 36 years in medicine, I think one of the most valuable things a good clinician does is not simply answer the question a patient brings into the room. Sometimes it is recognizing that we should have been asking a different question all along.

The post Helen Mirren Says She Uses AI Instead of Going to the Doctor. That’s Not the Part That Interests Me. appeared first on Medika Life.

Amid thousands of pilots, predictions and promises about AI, three health leaders brought something valuable to the conversation: ethics and experience. Hal Wolf, FHIMSS, Isaac Kohane, MD, PhD, and Ran Balicer, MD, MPH, PhD, have spent decades thinking about how information, technology and balanced human judgment can improve people’s health. Each approach challenges from a unique vantage point.

I had the privilege of moderating their HIMSS “Views from the Top” conversation about recognizing value when selecting AI applications. The subject may sound technical. The conversation was warm, personal and reflective. At its heart was a very human question: How do we make certain these extraordinary new capabilities actually improve people’s health?

Three Must-Hear Voices

The people leading this conversation matter. AI in health does not need more prediction for prediction’s sake. It needs experienced leaders who understand how innovation moves from possibility to policy, from data to decisions, and from scientific discovery to patients’ lives and into health professionals’ decision-making.

Hal Wolf, FHIMSS, sees health from a global systems perspective. As President and CEO of HIMSS, the world’s largest professional society for health informatics, he works with health systems, technology leaders, governments and policymakers around the world. Under his leadership, HIMSS has forged agreements with governments and public institutions to strengthen digital use and the infrastructure needed to make health information accessible and actionable.

Those relationships give Wolf a view few people have. He sees innovation across institutions, borders and policy environments. He also understands that technology does not transform health simply because it exists. Progress depends on whether policy, infrastructure, information and people can align to put innovation to work.

Isaac Kohane, MD, PhD, has spent his career asking difficult questions at the intersection of medicine, science, computation, ethics and patient care. As Chair of Biomedical Informatics at Harvard Medical School, the physician-scientist has helped shape the field that many health leaders are now trying to understand at extraordinary speed. At the foundation of his medical training and perspectives is his training in pediatric endocrinology.

His questions matter as much as the answers. What assumptions are hidden inside these systems? What happens when we use powerful technology to automate practices that were flawed to begin with? How do we distinguish an impressive computational result from something that creates genuine clinical value? Dr. Kohane keeps the focus on consequences: What happens when technological capability outpaces human understanding?

Ran Balicer, MD, MPD, PhD, sees patterns and possibilities in health information and understands how to put them to work. At Clalit Health Services, he works within one of the world’s largest integrated health organizations, where longitudinal information across millions of people can reveal connections that might never be apparent during an individual clinical encounter.

For Dr. Balicer, accumulating data is not just about knowing more. It is recognizing risk sooner and turning information into insight that leads to informed action. That can mean preventing a hospitalization, identifying someone who needs attention or giving a health professional information that could improve or save a life.

That is what made their HIMSS conversation so valuable. Rather than predicting an AI-defined future, they explored what experience is already teaching us: How to evaluate innovation thoughtfully, translate information into action and ensure that greater technological capability advances the health of the people and communities we serve. Here are 10 takeaway lessons from their conversation to carry forward and apply in planning.

HIMSS 2026 “Views From the Top” conversation with Hal Wolf, FHIMSS, Isaac Kohane, MD, PhD, and Ran Balicer, MD, PhD, moderated by Gil Bashe.

1. AI Is Already Here

Health organizations may still be debating their formal AI strategies while clinicians, employees and patients are already “vigilantes” using the technology. Dr. Kohane captured the tension well: Health systems can move too slowly and too quickly at the same time. Formal governance may proceed cautiously while people inside those same systems reach for readily available AI tools to solve problems in real time.

That changes the leadership question. It is no longer a question of whether AI will enter health.  That ship sailed long ago. The task is to understand how it is being used, where it adds value, where it creates risk and how leaders can guide its adoption responsibly.

2. Speed Still Needs Structure

Wolf has seen health technology move through transformational cycles before. When internet connectivity entered hospitals, clinicians wanted access before many institutions had built the infrastructure and security systems to support use at scale. People found workarounds because the need was pressing and governance had to catch up.

AI is moving much faster. The answer is not to freeze innovation until every question has been resolved. Health organizations need structures that allow people to move with greater confidence. Responsible governance should enable progress, not become another source of friction.

3. AI Requires Human Values

Dr. Balicer raised an idea that belongs in every room where an AI decision is being made: Algorithms contain opinions embedded in code. Every model reflects choices about what information matters, which outcomes are prioritized, what trade-offs are acceptable and how clinical success is defined.

We should worry about bias in data. We also need to look at the assumptions built into the technology itself. Selecting an AI system, therefore, requires more than asking whether it is accurate. Leaders need to understand whose priorities it reflects and whether those priorities align with the needs of health professionals, patients and public health systems.

4. Begin With the Problem

Anyone who walked the expansive HIMSS exhibit floor saw the investment companies are pouring into AI-supported technologies. The temptation is to begin with capability: Look what this can do. Wolf’s perspective points toward a better starting place: What health-system and patient-care problem are we trying to solve?

Health has accumulated decades of technology intended to create efficiency, sometimes producing another screen, another workflow, or another demand on a health professional’s time. AI cannot become the newest layer of complexity. Begin with the health problem, understand the outcome that matters and determine how success will be measured. Then decide whether AI can help.

5. Do Not Automate What Is Already Broken

Dr. Kohane offered one of the most memorable cautions of the conversation: AI could effectively pour concrete over some of the worst practices in medicine. That image stays with you because it captures the danger of assuming automation equals improvement.

A poorly designed workflow does not necessarily become better because it becomes faster. Misaligned incentives do not improve because intelligence is layered onto them. Fragmentation does not disappear because information moves more quickly. AI allows health to reconsider old processes rather than simply automate them.

6. Leaders Need to Get Their Hands Dirty

Dr. Kohane provided wonderfully practical counsel to executives: Use several AI models yourself. Ask them questions. Compare their responses. See where they agree and where they wander. Experience how remarkably confident an incorrect answer can sound.

Health leaders do not need to become computer scientists. They do need curiosity and enough direct experience to ask better questions. Leadership becomes difficult when our understanding of a transformative technology comes entirely through presentations, dashboards and other people’s interpretations.

7. Prediction Creates Responsibility

Dr. Balicer brought the conversation squarely into population health. AI increasingly allows health systems to identify people at risk before they arrive at a physician’s office. Imagine recognizing months earlier that someone may be headed toward hospitalization or a preventable complication. That is the promise health has discussed for decades: moving from reaction toward prevention.

The challenge arrives with the insight. What happens when a system identifies more people who could benefit from intervention than it has resources to help? Better prediction does not eliminate the challenges of access to care. It makes choices more visible. Knowing earlier also creates a responsibility to decide what we will do with that knowledge.

8. Give Time Back to Healers

Administrative AI matters. Reducing documentation burden, improving scheduling and making information easier to retrieve can return precious time to health professionals. The more important question is, how is that added time going to be used?  This is a critical decision that leaders and health professionals need to resolve.

If AI gives a physician another 30 minutes, does it create an opportunity to listen more closely to a patient, explain something more clearly or go home a little earlier after a difficult day?  Will institutions see this as an opportunity to schedule another two patient appointments? Does it reduce cognitive burden, or does the schedule simply absorb another appointment? The value of efficiency should ultimately be measured by what it gives back to people.

9. Move Beyond Pilots

The health system loves pilots. They let organizations explore, generate enthusiasm and manage risk. They can also become comfortable places to remain when the harder decision is determining what deserves to scale.

AI maturity requires choices. Which applications improve outcomes? Which reduce friction? What enables health professionals to make better decisions? Which secures wider trust? Which should be stopped? Pilots offer baseline experiential information. Progress comes when leaders are willing to act on what they learn and scale.

10. Technology Amplifies Our Choices

AI can find patterns that people might take months or years to see, summarize volumes of information that few clinicians have time to read and identify risk earlier than traditional systems allow. Those capabilities are extraordinary. They still cannot decide what kind of health system we want to create or when a diagnosis may overwhelm a patient who needs added time with a health professional.

That responsibility remains ours. Wolf’s experience reminds us that progress requires systems and policies capable of supporting innovation and the people within that institution. Dr. Kohane reminds us to question assumptions and subject technological capability to scientific and ethical scrutiny. Dr. Balicer shows what becomes possible when information is connected across populations and transformed into insight, enabling people to act sooner.

Their experience and perspectives do not provide every answer. They do something equally valuable by sharpening the questions health leaders must be asking. What problem are we solving? What evidence demonstrates value? What assumptions are embedded in the technology? Who benefits? Who might be overlooked? What will we do with the time, information and predictive capability AI returns to us?

These are leadership and governance questions more than technology questions. The conversation about AI in health is moving beyond whether machines can perform well at their tasks. We know they can (even with their false flags). Our opportunity is to connect that capability with human judgment and care, operational realities, and an understanding of the people we serve, whose lives depend on ethical and clinical decision-making.

Will AI Replace the Doctor?

That question is getting louder. Christina Farr recently brought Ezekiel (Zeke) Emanuel, MD, PhD, and John Whyte, MD, MPH, together on her podcast series, Lifers, to debate whether AI could replace physicians in significant areas of medicine by 2030.

Perhaps the more important question is not whether AI will replace health professionals. Wolfe and Drs. Kohane and Balicer offered a roadmap to explore that question: “It is what we should allow AI to replace, what we should insist it improve and what must remain fundamentally human.”

AI may eventually outperform people at an expanding number of clinical tasks. The measure of progress should be whether those capabilities improve health while giving health professionals greater capacity to do what people still need from people.

Technology will continue to become more informed. Our responsibility is to become wiser about how we use it, remembering that every data point ultimately represents a person counting on us to make the right choice.

The post 10 Things Health Leaders Need to Consider About AI Now appeared first on Medika Life.

Jobs was not talking about hospitals, clinical workflow, artificial intelligence or digital health. He was talking about where innovation must begin, not with technology, but with customer experience.

His point was simple and demanding. You cannot start with the technology and then figure out where to sell it. You have to start with the experience you want people to have, then work backward to the technology, systems and decisions required to make that experience possible.

That lesson belongs at the center of health innovation.

Too many promising companies enter the health sector leading with sophisticated platforms, powerful algorithms, elegant data architecture or novel science. Those strengths matter. However, they are not where adoption begins. Adoption begins with the people expected to use, believe in, approve, pay for, or benefit from the solution.

For health-sector entrepreneurs, the starting question cannot be, “What can our technology do?” It has to be, “What experience are we trying to create for the patient, clinician, researcher, administrator or institution we seek to serve?”

This is becoming increasingly visible as digital tools converge with therapeutics and clinical trials become more dependent on digital solutions. The question is no longer only whether the technology works. It is whether the experience works for patients, clinicians, researchers and institutions.

That is where entrepreneurial friction in health begins: not as an obstacle to creativity, but as a test of whether innovation has been shaped by the needs of the people who must use, have confidence and adopt it or by the capabilities of the technology itself.

From Fragmentation to Friction

For years, I have written about fragmentation throughout health. Long before fragmentation became a common buzzword at conferences or board meetings, it was evident that disconnected systems, competing incentives and isolated decision-making were creating unnecessary barriers for patients and clinicians alike. Fragmentation described the architecture of the problem.

Increasingly, I believe friction better describes the human experience of that architecture.

Fragmentation explains why organizations struggle to work together. Friction explains what physicians experience when they document the same information repeatedly, what nurses experience when technology disrupts rather than supports their workflow, what patients encounter when they navigate disconnected systems and what entrepreneurs discover when promising innovations stall within institutional bureaucracy.

Health professionals know this concern well from years of implementing electronic medical records. Too often, technology introduced to organize care has added clicks, documentation burden, and screen time, reminding innovators that adoption depends not only on what a system can do, but also on what it asks clinicians to absorb.

Every unnecessary approval, incompatible technology platform, duplicate workflow, unclear responsibility and poorly communicated decision creates resistance. None of those obstacles improve patient care. Each one slows the movement of innovation from discovery to implementation.

Health does not suffer from a shortage of remarkable ideas. Every week brings advances in artificial intelligence, biotechnology, precision medicine, diagnostics and digital health. Many of these innovations demonstrate meaningful improvements in clinical outcomes. Far fewer become part of everyday practice because the institutional friction surrounding implementation often receives less attention than the science itself.

Communication is a Key Implementation Strategy

Many founders in health start-ups are rightly fluent in science, engineering, data and clinical logic. That expertise is essential. The risk is that the human pathway to use receives less attention: the relationships, explanations and confidence-building that help patients, clinicians, administrators, payers and institutions understand how a new solution fits into their world. Without that connection, even strong ideas can meet resistance that looks like reluctance but often reflects an avoidable gap in understanding.

One misconception continues to undermine otherwise promising innovation. Communication is often viewed as beginning only after the product is complete. Marketing launches the announcement. Public relations introduces and positions innovation. Internal communications explain the rollout. That sequence misunderstands the purpose and impact of communication.

Communication is not simply how organizations describe innovation. Communication helps institutions understand change, reduce uncertainty and build the confidence required for adoption. It belongs alongside engineering, clinical research, workflow design and implementation planning from the earliest stages of development.

Consider a company that develops an artificial intelligence platform capable of reducing radiology turnaround times while maintaining strong clinical accuracy. The evidence is compelling. Independent validation supports the findings. Investors celebrate the technology’s potential.

Implementation nevertheless slows because department leaders worry about governance, radiologists question liability, information technology teams raise cybersecurity concerns and administrators remain uncertain about workflow integration. None of those questions challenge the supporting science. Each reflects uncertainty that could have been anticipated and addressed much earlier through supporting evidence and communication.

Consider another example. A digital platform helps people living with diabetes remain engaged between office visits, improving adherence and strengthening patient self-management. Physicians initially hesitate because they worry the technology will dramatically increase after-hours patient messages. Once the implementation team demonstrates automated triage, clearly defined clinical responsibilities and realistic workflow expectations, enthusiasm begins to replace skepticism. The technology itself remains unchanged. Understanding changes, interest grows and institutional friction begins to ease.

Communication does not replace implementation. It is part of the implementation. It turns complexity into shared understanding, aligns the people who must approve, use, pay for change, and reduces the friction that market fragmentation creates. Without communication, even beneficial innovation can remain trapped between promise and practice.

Designing Innovation for the Real World Experience

Jobs’ lesson should not be reduced to a technology slogan. It is honed and relentless discipline. Start with the experience and work backward. Keep asking whether each decision brings the user closer to value or pushes the organization deeper into layers of complexity. As Stephen R. Covey advised, “begin with the end in mind.” In health innovation, that end is not the technology itself. It is the experience, confidence and value created for the people expected to embrace and engage.

Health entrepreneurs should apply that relentless discipline within their own organizations by encouraging healthy debate among engineers, clinicians, patients, operational leaders and communicators. Diverse perspectives almost always produce stronger solutions because they test assumptions before the market, hospital, physician, or patient is forced to do so.

Equal attention should be devoted to eliminating the destructive friction that appears once innovation enters health institutions. Entrepreneurs should ask how many additional clicks a physician must complete, how many approvals a hospital must obtain, how easily the innovation integrates with existing systems, and whether every stakeholder understands not only what the innovation accomplishes but also how it improves everyday practice.

That is why one experienced health innovation champion, Levi Shapiro, founder and curator of mHealth Israel, a community of more than 20,000 health entrepreneurs, frames the challenge this way: “Clinical results and physician enthusiasm are table stakes. To overcome the ‘death by PILOT’ trap, the technology should integrate seamlessly into existing workflows, harmonize with operational and security requirements and demonstrate measurable ROI with minimal oversight. The technologies that scale are usually the ones that make adoption feel manageable, not disruptive.”

The future of health innovation will not be defined solely by better algorithms, more sophisticated diagnostics or increasingly powerful therapeutics. Success will belong to organizations that recognize implementation as a discipline requiring leadership, operational design, communication and empathy. Scientific excellence opens the door. Institutional readiness determines whether anyone walks through it.

Some friction strengthens thinking and encourages excellence. Other friction creates delay, confusion and unnecessary resistance. Recognizing the difference may become one of the most important responsibilities facing health entrepreneurs, institutional leaders and communicators alike.

Health does not face an innovation deficit. It faces an implementation deficit, made worse when communication is treated as an afterthought. Reducing the friction between what technology can do and what people need to experience may prove to be the next great breakthrough.

The post The Friction Between Innovation and Experience appeared first on Medika Life.

But this year, because of a new Medicare program, Curry has traveled a little more often.

In February, during one trip, he was told unexpectedly that he needed preapproval for the procedure. Then he went again a month or so later to get the injection, for a total of 10 hours on the road. His clinic wanted him to come in a third time, which they had never asked of him before. That appointment was “just to fill out a piece of paper to tell them how you feel again,” Curry said, so he hasn’t gone.

In January, Oklahoma became one of six states to begin a pilot program testing the use of preapprovals in traditional Medicare, the federal health insurance program for people 65 and older or with disabilities. Medicare had previously eschewed the practice — also known as prior authorization — which requires patients or someone on their medical team to seek insurance approval before proceeding with certain procedures, tests, and prescriptions.

Epidurals like Curry’s are among 13 medical services subject to the new program because the Trump administration says they’re prone to fraud or misuse. Powered by artificial intelligence, the program — called the Wasteful and Inappropriate Service Reduction Model, or WISeR — is intended to save the federal government money and protect patients from potentially unsafe or unneeded care.

Yet early reviews from Oklahoma and the other pilot states — Arizona, New Jersey, Ohio, Texas, and Washington — suggest WISeR’s rollout has not been smooth. Patients, doctors, and other healthcare professionals who spoke with KFF Health News say the effort has created confusion, errors, long wait times, and stress. Some described the rollout as “horrendous” and say people enrolled in Medicare in the pilot states are now getting ensnared in the same red tape as those with private insurance.

One key concern is that it all happened too hastily. WISeR was announced in June 2025 and launched in mid-January.

That was “quicker than normal” for the federal government, said Todd Baker, who recently stepped down as CEO of the Ohio State Medical Association. Doctors “just sort of had to figure it out,” added Jeb Shepard, director of policy at the Washington State Medical Association.

Government contractors have also acknowledged the rapid pace. “We’ve had an aggressive rollout from the time of being notified to going live,” said Jeremy Friese, CEO of Humata Health, the vendor for Oklahoma. Tech executives servicing other states have said they were still adding features to their products in the spring.

Abe Sutton, director of the Center for Medicare and Medicaid Innovation, which is administering the program, didn’t comment on the rollout schedule. But he said in a statement that the goal of these reforms is to ensure that prior authorization is efficient, fast, and streamlined.

“The model aims to reduce inappropriate care without delaying appropriate care,” he said.

Mehmet Oz, the leader of the Centers for Medicare & Medicaid Services, told NewsNation in December that they were “rolling out some prior authorization on abused practices.”

“The purpose of these is not to deny care,” Oz continued. “It’s to make sure you get the care you need and deserve, not the care some unscrupulous doctor wants to use on you.”

Medicare has struggled in recent years with suspected fraud associated with particular services. The Department of Health and Human Services’ inspector general warned in September that the program’s spending on skin substitutes, for example, had surged nearly 700% over two years, raising “major concerns about fraud, waste, and abuse.” Skin substitutes are among the 13 therapies currently subject to review under WISeR.

The program also imposes prior authorization requirements for kyphoplasty, a surgery for spinal fractures, which a report by the Medicare Payment Advisory Commission flagged as overused.

Sutton acknowledged, however, that “the percentage of providers committing waste, fraud, and abuse is small.”

Consumers and clinicians largely detest prior authorization. Even as federal health officials test the process for Medicare, the Trump administration is trying to scale it back for those with private insurance. According to a KFF poll conducted in January, 69% of insured adults consider prior authorization a burden for care.

Through WISeR, doctors and their staff log in to online portals to submit medical records that justify the procedures. Using artificial intelligence, the systems quickly approve applications that meet the program’s criteria, Friese, Humata’s chief executive, told KFF Health News. He said there is an “immediate yes” in 88% of cases for which clinical data supports an approval.

CMS has touted the process as one in which decisions are returned within 72 hours. After that, clinicians receive a “universal tracking number,” which allows them to schedule the procedure and get paid. In practice, however, participants say the process is anything but easy.

The University of Washington’s medical system alone had nearly 100 patients waiting earlier this year for epidural injections due to WISeR-related delays, according to an April report from the office of U.S. Sen. Maria Cantwell (D-Wash.) that drew on hospital association data. “Now, patients are subject to delays or denials which did not exist prior to the WISeR Model,” the report said.

Curry, the Oklahoma cattle farmer, said he might go to Kansas for future treatments to avoid the approval process. Dorota Gribbin, a New Jersey-based physical medicine and rehabilitation physician, said that by the time authorization came for one of her patients who needed a back pain procedure, the patient had gone to the hospital for more expensive care.

Jennifer Valle, a precertification and insurance supervisor at Clinical Radiology of Oklahoma, said when it comes to kyphoplasties, there has been a lot of “nitpicking” from reviewers. Other times, information her practice provides to CMS gets overlooked, she said, and reviewers ask for imaging that’s already in the file.

Claims with no problems are supposed to be paid within 15 days, said James Webb, a musculoskeletal radiologist in Tulsa, Oklahoma, who has also been frustrated by the prior approval and reimbursement process for kyphoplasties. “Six- to eight-week delays is what we’ve been seeing,” he said.

“It’s been horrendous,” said Jerry Sobel, a Phoenix-area pain management doctor. “Right from the beginning, there seemed to be no organization.” Sobel said that as of May, he hadn’t gotten paid by Medicare for nine epidurals.

“We continuously monitor operations and work closely with stakeholders to address questions and improve the provider experience,” said Sundar Subramanian, the CEO of Zyter, which has the contract for Arizona.

During an April webinar, another Zyter executive acknowledged a large backlog in payments stretching to January. Those backlogs “are currently being resolved,” Medicare’s Sutton said, without providing further detail.

When asked about other issues — including what doctors suspect are AI-driven errors — Medicare’s Sutton said the agency appreciates “feedback on provider experience.” It will be used “to help providers better understand WISeR processes,” he said.

Although CMS vendors say humans make the final decisions on approvals, doctors and their staffs believe artificial intelligence is playing a large role in the process and that denials are sometimes the result of AI hallucinations that garble or make up information.

One Arizona doctor, who wasn’t authorized by his practice to speak, recalled a denial saying his patient wasn’t eligible for procedures in the thoracic region, or mid-back. The patient needed an injection to the neck. Webb, the Oklahoma radiologist, documented four times that a patient lacked numbness, and yet his WISeR application was still denied, citing numbness, which, in the reviewer’s interpretation, would rule out the spinal surgery procedure.

Friese, Humata’s CEO, said he hasn’t heard about any AI hallucinations.

The process is also raising government costs. With more rejections, more appeals are being filed with Medicare’s administrative contractors. The government pays the contractors to handle the appeals, and Medicare’s Sutton acknowledged that the agency has “accounted for potential changes in the volume of Medicare appeals because of the WISeR program and its associated costs.”

Eighty-four percent of commercial insurers already use AI tools, according to a survey released in 2025 by the National Association of Insurance Commissioners, though they have consistently said AI isn’t used to deny prior authorization requests.

Its use in Medicare risks introducing friction and frustration into the program — and piling costs onto its beneficiaries. Prior authorization saves money for insurers partly by making patients pay a price in wait times and inconvenience, said Miranda Yaver, a University of Pittsburgh health policy researcher studying the technique.

“People will end up getting ensnared in a lot of red tape, having to be on hold, and getting rerouted,” she said. She often wonders whether prior authorization simply shifts costs to patients and doctors, rather than saving them.

Some doctors involved in Medicare’s prior authorization experiment believe it will inevitably expand beyond a few services officials in Washington consider fraud-prone.

“Everybody knows that if this pilot project works, it will be prior auth for basically all procedures,” said Mary Clarke, a family practice physician in Stillwater, Oklahoma. “If they can show that they can save money, then that’s going to be extrapolated and rolled out to other procedures and multiple other things in other states.”

When asked whether CMS is considering expansion of its prior authorization pilot, Sutton said in his statement that there are “currently no changes” considered for the list of services subject to the WISeR program, “but CMS continues to assess whether any changes are warranted.”

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Its leaders were visible throughout the convention, participating in discussions on biopharma innovation, precision medicine, artificial intelligence, investment, genomics and policy. Partnership announcements emerged throughout the week. Delegations moved between panel discussions and private meetings with investors, entrepreneurs, researchers, and industry leaders. The message was clear. Abu Dhabi is making a strategic effort to become a notable player in biotechnology and the life sciences.

The timing is not accidental.

Around the world, governments increasingly view biotechnology as a strategic investment industry. Scientific innovation drives economic growth. Advanced therapeutics create new manufacturing opportunities. Genomics and precision medicine are reshaping approaches to disease prevention and treatment. Nations that attract talent, investment and scientific expertise position themselves at the forefront of one of the century’s most consequential industries and life-sustaining movements.

Abu Dhabi is determined to be among those countries.

Building the Innovation Foundation

Substantial investments and partnerships in infrastructure, research, education and health innovation support the Emirate’s ambitions.

In April 2025, Abu Dhabi launched the Health, Endurance, Longevity and Medicine (HELM) Cluster, an initiative designed to establish a globally competitive ecosystem spanning biotechnology, pharmaceuticals, medical technology, genomics, digital health, artificial intelligence and advanced manufacturing.

Officials project the initiative will contribute AED 94 billion to the economy by 2045, attract more than AED 42 billion in investment and create approximately 30,000 jobs.

Those figures reflect more than economic development goals. They signal a growing recognition that biotechnology is a cornerstone of future growth and global competitiveness.

The Emirate is not starting from scratch. Organizations including M42, PureHealth, Mubadala Bio, Khalifa University, and the Mohamed bin Zayed University of Artificial Intelligence have established a foundation that combines research, clinical capabilities, advanced analytics and investment resources. The UAE Genome Program has surpassed 900,000 sequenced genomes, making it one of the world’s largest population genomics initiatives and providing a valuable resource for scientific research and precision medicine.

These investments provide the ingredients necessary to compete. Partnerships provide the opportunity to accelerate progress.

Building Through Collaboration

Biotechnology has long been a collaborative enterprise among private equity and entrepreneurs, academic medicine and corporations, and, now, nations working side by side with other countries’ governments.

Scientific discovery depends on the exchange of knowledge among researchers, entrepreneurs, clinicians, manufacturers, regulators, and investors. No single country possesses every advantage. Successful ecosystems learn how to connect their strengths with those of others.

Abu Dhabi’s recent actions suggest its leaders understand this reality more than anyone else. They act on it.

During the BIO International Convention, the Department of Health – Abu Dhabi announced a strategic collaboration with Sanofi focused on vaccine development and life sciences innovation. The previous year at BIO in Boston, the Department established a partnership with Boehringer Ingelheim that expanded access to the company’s OpnME research platform, creating new opportunities for translational research and scientific discovery.

An additional 2025 agreement was signed with Abbott, focused on pharmaceutical innovation, manufacturing capabilities, and emerging technologies.

These were not isolated announcements. They represented a broader effort to connect Abu Dhabi with leaders across the global life sciences community. That strategy continued at BIO 2026.

On June 23, 2026, the Department of Health – Abu Dhabi announced a strategic partnership with Biocom California, one of the world’s largest life sciences associations representing more than 1,800 biotechnology, pharmaceutical, and medical technology organizations. The agreement creates a formal gateway between the California innovation ecosystem and Abu Dhabi’s growing life sciences sector, strengthening opportunities for collaboration among researchers, entrepreneurs, investors, and innovators across both markets.

The significance of the announcement extends beyond California. It reflects Abu Dhabi’s effort to connect itself to some of the world’s most influential innovation networks and to participate in the exchange of scientific knowledge, talent, and investment that increasingly defines biotechnology leadership.

Moving Up the Biotech Value Chain

If the Biocom agreement demonstrated Abu Dhabi’s commitment to global collaboration, a second announcement made the Emirate’s ambitions even clearer.

On June 24, 2026, the Department of Health – Abu Dhabi, M42, and Mammoth Biosciences announced a partnership to advance gene-editing therapies, clinical research, and advanced therapy manufacturing in Abu Dhabi. The agreement seeks to leverage insights generated through the Emirati Genome Program while supporting the development of next-generation treatments for inherited diseases.

This announcement stands out because it moves beyond ecosystem building and into the development of future therapies.

Mammoth Biosciences was co-founded by Nobel Prize-winning scientist Jennifer Doudna, whose pioneering work helped bring CRISPR gene-editing technology into medicine. Under the agreement, Mammoth will contribute its proprietary gene-editing platform, M42 will provide genomics, health, and clinical research infrastructure, and the Department of Health will support the regulatory and research environment needed to advance development.

The collaboration includes plans to introduce Mammoth’s lead clinical candidate, MB-111, into Abu Dhabi’s research ecosystem, support advanced gene-editing clinical trials, establish advanced therapy manufacturing capabilities, and develop local expertise through workforce training programs.

Perhaps most significantly, the partnership highlights how Abu Dhabi is leveraging its genomics investments. The Emirati Genome Program has created one of the world’s most comprehensive population genomics initiatives. The Mammoth agreement represents an effort to translate those insights into therapies targeting inherited diseases.

As H.E. Dr. Noura Al Ghaithi noted when announcing the partnership, Abu Dhabi is focused on translating genomic insights into therapies to address some of the most complex inherited diseases affecting populations in the region and worldwide.

Leadership Matters

The prominence of Abu Dhabi at BIO 2026 reflects sustained engagement from senior leaders. H.E. Dr. Noura Khamis Al Ghaithi, Undersecretary of the Department of Health – Abu Dhabi, and Dr. Mohamed Al Ameri, Division Director of Genome and Biobank at DoH, are among the officials representing the Emirate’s vision throughout the convention.

Their participation reflects a broader commitment. During Abu Dhabi’s 2025 strategic mission to the United States, approximately 40 representatives from 12 organizations attended more than 20 strategic meetings, conducted 16 institutional visits, participated in 9 BIO-related panels, and established 7 new partnerships and agreements.

Such activity underscores an important point. Building a biotechnology ecosystem requires more than investment capital. It requires leadership, patience, and a willingness to build relationships across borders and disciplines.

A Must-Watch Innovation Hub

For decades, discussions about biotechnology leadership have focused on a familiar collection of cities and regions. Boston, San Diego, Basel, London, and Singapore earned their positions through scientific excellence, entrepreneurial activity, and investment.

Abu Dhabi is pursuing a different path.

The Emirate is leveraging capital, scientific infrastructure, genomics, artificial intelligence, policy support, and international partnerships to establish a presence in the global biotechnology landscape. Its strategy recognizes that modern biotechnology advances through collaboration and that scientific leadership increasingly depends upon connecting talent, expertise, and resources across borders.

BIO 2026 demonstrated that Abu Dhabi is no longer simply expressing an ambition to participate in the life sciences sector. Through initiatives such as the HELM Cluster, partnerships with organizations including Sanofi, Boehringer Ingelheim, Abbott, Biocom California, and Mammoth Biosciences, and investments spanning genomics, research, and advanced therapies, the Emirate is laying the foundations for a biotechnology ecosystem with global aspirations.

Whether Abu Dhabi joins the ranks of the world’s leading life sciences hubs remains to be seen – but it should be watched closely. What is increasingly difficult to overlook is the depth of its commitment and the speed with which effort is being translated into action.

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The body remains, but the self doesn’t.

We understand something in those moments that we rarely say plainly. And perhaps, it’s time we put this idea front and center. Cognition isn’t merely something a person has, it’s something a person is.

Day after day, we become ourselves through the act of thinking. From the complex to the trivial, we traverse a reality that bumps and bruises us into personhood. And that friction isn’t an obstacle to identity, it’s how identity forms.

Aristotle understood this long before neuroscience provided a name for it. Character isn’t something we possess. It is something we create. What we think shapes what we do. What we do, repeatedly, shapes who we become. Which is why the question of artificial intelligence, at least to me, isn’t primarily a question about productivity or efficiency.

Of course, AI doesn’t arrive as a threat, it arrives as a relief. And that’s what makes it so insidious. There’s no cognitive check engine light to warn you. There’s just the comfort of a swift and almost effortless answer. The friction that used to shape you simply didn’t happen. Do that enough times and something changes, not dramatically, but in the way that habits shift things. Gradually, then all at once.

Technology has always extended human capability. The wheel extended our legs. Writing extended memory. The calculator extended arithmetic. But AI is different in kind, and not merely degree. It reaches into cognition itself, into the territory where “we” live—into the domain of judgment, understanding, and idenity. A calculator doesn’t threaten to do your becoming for you.

The neuroscientist Michael Merzenich is well-known for the mechanism that we today call neuroplasticity. Simply put, neural connections are strengthen when used and weakened when not. The brain adapts continuously to the demands placed upon it. This isn’t a lofty metaphor but measurable biology. The brain you exercise is not the brain you don’t.

But cognitive surrender isn’t a neutral act. Every decision handed off to AI are small withdrawals from the account of the self. Of course, handing the process over to a machine provides certain efficiency or even relief, but you step away from the mechanism through which you author, well, you.

There is a phrase, adapted from the Upanishads, that I alluded to earlier: as you think, so you act. As you act, so you become. This is doing more than describing habit. It is describing identity formation. We are not simply what we know. We are, in part, what we have struggled to understand.

The answers may still sound like you. What fills the space is not.

That’s the abyss. Not a dramatic fall, but a quiet retreat from the very process that makes a person a person.

I wrote about the Borrowed Mind as a possibility. Now, I think it’s worth asking, with some regularity, whether it has become a habit.

John Nosta is the author of the best seller:  The Borrow Mind—Reclaiming Human Thought in the Age of AI.

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The Exhibition Floor as a Market Signal

However, after several days walking the exhibition floor and listening to discussions across multiple stages, another story emerged. The most interesting development at HLTH Europe was not the continued rise of AI. It was the growing number of companies applying artificial intelligence to solve very specific challenges faced by researchers, physicians, health systems and patients.

What appears on the stages and exhibition floor at HLTH often reflects where the market sees opportunity. Conferences do not create trends. They reveal them. HLTH Europe brought together more than 400 speakers, some 350 sponsors and approximately 5,000 participants from across the global health ecosystem. Artificial intelligence was not simply one topic among many. The conference featured a dedicated AI @ HLTH Zone, AI-focused exhibitors and numerous sessions exploring implementation, governance, clinical applications and operational adoption.

The prominence of AI across both the agenda and exhibition hall was revealing. Conference organizers dedicate space and programming to topics that matter to attendees, investors and sponsors. The visibility of AI at HLTH Europe suggested that health-specific applications of artificial intelligence have moved beyond emerging interest and are now a significant market focus.

That shift matters because health has always demanded more than technological capability. New tools must operate within environments where privacy, safety, accountability and trust are essential. Researchers are looking for ways to accelerate discovery. Physicians want to reduce administrative burdens that consume valuable time. Health systems seek efficiencies that improve operations without compromising quality. Increasingly, innovators are designing AI solutions around those specific needs.

That reality helps explain why many of the most compelling AI companies at HLTH Europe are building solutions specifically for health rather than adapting tools designed for other industries.

As Sophie Taylor-Roberts, managing partner and FINN Partners UK Health Group Lead, shared: “A mistake in healthcare carries a human cost: it can literally mean life or death. That’s why healthcare needs bespoke AI models, tools and solutions that allow for diverse patient populations, differing clinical guidelines, funding and regulatory structures.”

She added, “As with all aspects of health, one size doesn’t fit all. AI must be treated like a highly specialized medical instrument, built to respect national sovereignty, multilingual patient care, and absolute data privacy.”

Health-Specific AI Moves from Possibility to Practice

The trend was visible throughout the exhibition hall, where companies focused on clinical research, physician workflow, diagnostics, patient engagement, digital safety and operational efficiency demonstrated how specialized AI is rapidly becoming a category of its own.

The trend was visible throughout the exhibition hall, where companies focused on clinical research, physician workflow, diagnostics, patient engagement, digital safety and operational efficiency demonstrated how specialized AI is rapidly becoming a category of its own. Their growth reflects a broader shift occurring across the health sector as organizations seek tools designed for specific scientific, clinical and operational challenges.

Gabriele Ricci, Chief Data & Technology Officer at Takeda, captured that evolution when discussing AI’s growing role across the research and development continuum. “AI is transforming the future of healthcare by accelerating every stage of the R&D value chain through purpose-built capabilities tailored to specific scientific and clinical challenges,” he said.

His emphasis on purpose-built capabilities mirrors what was visible throughout HLTH Europe. The conversation is no longer centered exclusively on artificial intelligence as a technology platform. Increasingly, attention is turning toward how specialized applications can address distinct needs across research, clinical care and health operations.

Among the companies reflecting this shift was Briya, whose AI-powered platform helps researchers interact with complex data through conversational interfaces. Rather than requiring users to navigate multiple databases, coding environments and analytical tools, the platform seeks to simplify the path from question to insight.

David Lazerson, Briya’s co-founder and chief executive officer, believes many organizations misunderstand where the greatest challenge in AI adoption resides.

“Many people assume AI adoption is about choosing the right model,” he said. “In reality, the model is only a small part of the solution. The hard part is everything around it: security, governance, data harmonization, domain expertise, and the methodology required to produce trustworthy outcomes.”

His observation reflects a reality becoming increasingly evident throughout the health sector. Access to powerful AI models is expanding rapidly, shifting competitive advantage toward organizations that can generate reliable outcomes within specific health environments. That reality helps explain the growing number of exhibitors focused on narrowly defined use cases rather than general-purpose AI.

A similar perspective emerged from conversations with Keith Grimes, MD, Chief Innovation Officer at Curistica. A physician who spent 24 years in primary care, Grimes approaches artificial intelligence through the lens of risk management, governance and patient safety.

“Physicians have always governed risk,” he explained. “We do it instinctively for doctors, drugs and devices. Digital is just the fourth D, and the discipline is much the same, but it is the one we were never trained for, so the commitment to ‘do no harm’ runs ahead of the know-how.”

His comments address one of the most significant challenges facing health organizations today. Many leaders recognize the promise of AI, yet remain uncertain about implementation, oversight and accountability, particularly in smaller physician practices and community-based care settings.

Dr. Grimes emphasizes that smaller organizations should not view those limitations as barriers.

“Small practices are the cornerstone of primary care, but they cannot out-resource a hospital trust, and it does not need to,” he said. “Good governance scales down, and the same standards that protect a large organization can be borrowed rather than rebuilt.”

“We give whoever is responsible for AI and digital safety both the platform and the people,” Dr. Grimes said. “Power tools that guide them, whatever their experience, with clinical safety experts behind the software.”

Taken together, the perspectives of Dr. Grimes and Lazerson point to the emergence of a new category of innovation. The most promising health AI companies are not focused exclusively on algorithms. They are creating environments that combine technology, expertise and governance to solve specific high-friction problems.

The Future Belongs to Reliable Outcomes

For smaller organizations, this evolution may prove particularly significant. Historically, adopting advanced technology often required substantial investment, specialized technical talent and complex integration efforts. Many health organizations lacked the resources to pursue those initiatives.

Lazerson believes that model is changing. “That’s why we’re seeing the emergence of a new layer of domain-specific AI,” he said. “Instead of every organization hiring AI engineers and building custom infrastructure, they can access a complete, purpose-built environment as a service.”

The implications extend far beyond research organizations. Physician practices, community health providers, home health agencies and emerging life science companies increasingly have access to capabilities that previously required significant internal resources.

“For smaller organizations in particular, it’s a no-brainer,” Lazerson added. “They can start generating value immediately without complex integrations, dedicated AI teams, or having to solve privacy, security, and compliance challenges on their own.”

Throughout HLTH Europe, companies focused on clinical research, workflow automation, diagnostics, care coordination and patient engagement demonstrated how artificial intelligence is becoming increasingly specialized. Rather than attempting to transform every aspect of health simultaneously, they are concentrating on areas where measurable value can be achieved quickly and responsibly.

That focus on practical outcomes may ultimately become the defining characteristic of the next generation of health innovation.

Dr. Grimes summarized the principle succinctly. “Safety is not a box-ticking exercise; it works when everyone knows the part they play,” he said. “The advantage is not scale, it is fit.”

Walking through HLTH Europe, I was reminded that innovation rarely advances through a single breakthrough. More often, progress emerges through focused efforts to solve meaningful problems. The companies attracting attention were helping researchers move faster, supporting clinicians facing administrative burdens and enabling organizations to adopt new capabilities with greater confidence.

Perhaps among the more important lessons from HLTH Europe. The future of AI in health will not be defined solely by the largest platforms. It will be shaped by innovators who combine technology, expertise, and specificity to deliver reliable outcomes. As Lazerson observed, “The future won’t belong to organizations with the biggest models. It will belong to those who can turn AI into reliable outcomes.”

Judging by what appeared across the stages and exhibition floor in Amsterdam, that future is taking shape.

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Bringing Conversational AI to Scientific Research

The announcement arrives as artificial intelligence continues to reshape nearly every corner of the health sector. Much of the attention has focused on applications designed for consumers seeking information or clinicians seeking support in managing increasingly complex workloads. Briya is directing its attention to a different priority audience: medical researchers in academia, hospitals and life science companies.

The decision reflects a recognition that scientific inquiry often remains constrained by barriers that have little to do with science itself. Researchers routinely navigate fragmented data sources, technical requirements, analytical platforms and resource limitations before they can begin testing a hypothesis or exploring an observation.

The Next Step in Briya’s Evolution

Briya is executing on the established understanding that artificial intelligence can help reduce those barriers.

Researchers using AIRE will be able to explore public health information, including data from the U.S. Centers for Disease Control and Prevention, through a browser-based conversational interface. Rather than writing code, users can ask questions in natural language, refine their inquiry through dialogue and review the analytical pathway used to produce results.

“The last few years proved that AI can generate answers,” Briya co-founder and CEO David Lazerson told Medika Life. “The next challenge is making AI capable of generating trustworthy science. That requires a fundamental shift from general-purpose AI systems to research environments built around transparency, epidemiological methodology and scientific accountability.”

The announcement represents the latest step in Briya’s evolution. Founded in 2020 by Lazerson and Chief Technology Officer Guy Tish, the company initially centered efforts on helping organizations connect fragmented health data while maintaining privacy protections, governance requirements and institutional control over sensitive information.

Medical records rarely exist in a single location. Information is often distributed across electronic medical records, laboratory systems, imaging platforms, physician notes and institutional databases. Briya developed a federated approach that allows information to remain within source organizations while supporting approved research across participating data environments.

AIRE expands that mission from data access to data exploration.

The platform is designed to support cohort construction, endpoint validation, treatment pathway analysis, chart review and the exploration of structured and unstructured clinical information. Researchers interact with the platform through conversation rather than code, allowing them to start with a scientific question rather than a technical workflow.

The strategy mirrors an approach that has proven successful in other areas of artificial intelligence. Consumer platforms such as ChatGPT and Perplexity accelerated adoption by allowing users to experience the value of AI before deciding whether additional capabilities justified a subscription.

Reducing the Distance Between Questions and Answers

Briya is applying a similar philosophy to research. Many health technology companies continue to pursue adoption through enterprise purchasing processes, institutional pilots and lengthy implementation cycles. The Briya approach places the researcher at the center of the experience and allows investigators to determine the platform’s value through direct, frequent use.

The company believes that approach may be particularly meaningful for researchers working outside large academic medical centers and major pharmaceutical companies. Those institutions often have access to dedicated data science teams and sophisticated analytical resources. Smaller universities, physician-scientists, public health investigators and community-based researchers may not.

The absence of resources does not diminish the importance of the questions they seek to answer. In fact, as many attending HLTH EU head from Amsterdam to BIO International in San Diego, many of the biggest life-changing advances start in smaller research settings.

Giving Researchers a Seat at the Table

A physician observing an unusual treatment response, a public health researcher investigating a local health pattern, or an early-career investigator evaluating a new hypothesis all face the same challenge: transforming observation into evidence. That process frequently requires technical expertise and infrastructure that are not universally available.

Reducing those barriers could expand participation in research and potentially broaden the range of questions being explored. Accessibility alone, however, is not enough.

Scientific inquiry requires transparency, reproducibility and methodological rigor. Researchers must understand how conclusions are reached, what assumptions influence an analysis and where potential bias may exist.

A Move from Observation to Evidence

Recognizing those requirements, Briya recently appointed internationally recognized epidemiologist Professor Jonathan Samet, MD, MS, as Chief Epidemiologist. Dr. Samet is Professor of Epidemiology and Occupational and Environmental Health, and the former Dean of the Colorado School of Public Health.

“Scientific rigor and accountability cannot be layered onto AI after the fact,” Dr. Samet told Medika Life. “If these technologies are going to play a meaningful role in healthcare research, transparency, reproducibility and epidemiological methodology must be built directly into the system itself.”

Samet added that researchers need to understand more than an AI-generated conclusion: “Researchers need to understand not only what an AI system concludes, but how it reached those conclusions and what risks may exist along the way.”

His appointment reflects a broader challenge facing artificial intelligence in research environments. While generative AI systems can produce clear and persuasive responses, researchers and institutions must be able to evaluate the methods, assumptions and analytical pathways behind those outputs.

Trust, governance and cybersecurity have become as important as speed and convenience. Health information remains among the most sensitive categories of personal data. Institutions considering AI-enabled research environments must evaluate privacy protections, security controls and governance requirements alongside scientific capabilities.

Briya says its architecture is designed to allow data to remain within source organizations while supporting anonymization, compliance controls and auditable pathways for approved analysis.

Briya’s decision to open access to AIRE arrives at a time when researchers are under increasing pressure to produce meaningful scientific output while navigating growing volumes of health information. The platform’s no-cost entry point reflects a broader shift occurring across technology, where organizations increasingly recognize that adoption begins with customer experience. By allowing researchers to engage directly with data through a conversational interface, Briya is reducing barriers that have traditionally separated scientific questions from scientific exploration and adoption.

The announcement broadens the conversation surrounding artificial intelligence in health. Much of the industry’s attention has focused on consumer and clinical applications. Briya is directing attention to another critical constituency whose work influences every future therapy, diagnostic and public health intervention.

As HLTH Europe begins, the company is making the case that empowering researchers may represent one of the most consequential applications of artificial intelligence in health. If successful, the approach could help accelerate discovery, expand participation in research and provide investigators with a direct path from observation to evidence to implementation.

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