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.

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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.

The post At HLTH Europe, the Most Important AI Story Was Happening Beyond the Headlines appeared first on Medika Life.

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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But today’s advanced digital machines differ greatly from early simple computers. The digital world aims not only to copy human memory but also to implement the structure of neurons and the complex mechanisms of human neurophysiology so that machines do not have to receive data from outside every moment and can hold the information needed to perform a task in an instant.

So far, the design of advanced digital machines seems to have worked well. Where is the problem? Why, as the digital industry advances, does the human–machine interaction still break down? Why does AI remain distant from the real world of human users, when these users—clients, patients, and service recipients—must trust the technology, share information with it, and receive information from it?

As a psychologist in the learning sciences, I believe the problem is the digital industry’s lack of attention to “human deep learning.”

Where does human deep learning come from?

Until now, attempts have focused on defining deep learning for machines, and designers claim that relying on neuroscience finishes the job. But if neuroscience were sufficient, we would see fewer challenges today. The problem is that data scientists often forget that the ultimate goal is the human being—and humans are the most complex creatures. Neuroscience is only a small part of the knowledge about the mind and human development. To design human-like machines (for example, in medicine, therapy, or rehabilitation), we must explore a much broader and more diverse range of dimensions of user learning.

I call this “human deep learning.”

Although the term “deep learning” may not appear explicitly in some learning and psychology literature, its ideas are present throughout rich scholarship in the learning sciences. Cognitivist approaches in learning science—which view learning as meaningful, durable understanding or as deep change in thinking—are aligned with deep learning. These approaches argue that when a human deeply learns a belief or concept, the change endures and transfers to different but similar situations. This kind of learning is purposeful and therefore shows up in learners’ behavior and performance.

Cognitive and learning theories that speak to the idea of deep learning include Gestalt theory, Piaget, Vygotsky, Bandura (cognitive-behavioral approaches), and others.

What should we do?

If we want to design digital machines’ deep learning with these perspectives in mind, we must focus much more on behavioral sciences and learning psychology. Then the problems of mutual understanding and interaction between machines and humans will become more manageable.

As noted earlier, AI layers have been built heavily on data science, and designers claim to mimic neuroscience, but human sciences are not limited to neuroscience. If interaction between humans and machines is to occur, the processes of meaningful human learning—seen through learning science—must be discovered and used to guide machine design.

To what extent have designers taken steps in this direction?

Conclusion: Redefining AI layers

It is time to reconsider the layers of artificial intelligence. In common models, AI foundations rest mainly on data, algorithms, machine learning, neural networks, and deep learning. But if the ultimate purpose of this technology is to serve and effectively interact with humans, we must add another foundational layer: the learning sciences.

In two recent papers I wrote for the HTLH Europe 2026 conference and published in MedikaLife , I addressed the importance of the learning sciences. The first, “Human-Centered AI in Digital Health: Why Learning Sciences Matter,” discussed why learning sciences matter in digital health, a key application area for AI. The second, “Operationalizing Learning Sciences for Human-Centered AI in Digital Health,” explained some practical principles for applying learning sciences in digital health.

In this article I tried to highlight the central connection between learning sciences and AI—what I call “deep learning.” For that reason I reshaped the common pyramids that describe AI layers according to my perspective and the arguments I presented in my papers, and I proposed a new pyramid.

Learning sciences are not just an academic field; they are the foundation for understanding how humans learn, decide, change behavior, and grow cognitively. At the core of this foundation are cognitive psychology, behavioral sciences, motivation, social learning, self-regulation, and meaningful learning.

This set is what I call “human deep learning.”

In this framework, the future evolution of AI (and all AI-linked domains such as digital health) should be modeled by a pyramid in which learning sciences and human deep learning are not a side layer but the foundation of the whole structure. If AI is to work effectively in medicine, education, mental health, and other human-centered fields, it cannot rely only on data and inspiration from neuroscience. The next generation of AI must learn that humans and their learning processes are central and foundational.

The next generation of AI must learn how humans learn.

References

Ferdosipour, A. (2026). How can instruction and learning lead to deep learning in the learner? The answer from Gestalt cognitive psychologists. The Learning Guild (Accepted/In Press, July 2026 publication).

Ferdosipour, A. (2026). Why AI needs Vygotsky: The case for AI-based intentional friction. The Learning Guild.

Ferdosipour, A. (2026). Human-centered AI in digital health: Why learning sciences matter. Medika Life. https://medika.life/human-centered-ai-in-digital-health-why-learning-sciences-matter/

Ferdosipour, A. (2026). Operationalizing learning sciences for human-centered AI in digital health. Medika Life. https://medika.life/operationalizing-learning-sciences-for-human-centered-ai-in-digital-health/

Ferdosipour, A. (2026). Why biological learning demands the friction we seek to delete? Medika Life.

Ferdosipour, A. (2026). The shift from pure modernity to human-centered modernity. Medika Life.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

Piaget, J. (1950). The psychology of the child. Basic Books.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.

Bandura, A. (1989). Social cognitive theory. Annual Review of Psychology, 40, 1–25.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning: A textbook. MIT Press.

The post Machine Deep Learning or Deep Learning of Humans? Which is Correct: “Machine Deep Learning” or “Deep Learning of Humans”? appeared first on Medika Life.

Health care is not short on ideas. It is not short on innovation, intelligence, technology or ambition. What it risks losing is focus on why those ideas matter and who they are meant to serve.

That concern shaped a fireside conversation with Tom Lawry at the Digital Health & AI Innovation (DHAI) Summit. Tom and I came to the stage from parallel and connected bodies of work. He is a best-selling author, the author of Health Care Nation: The Future Is Calling and It’s Better Than You Think, Hacking Healthcare and his classic, AI in Health: A Leader’s Guide to Winning in the New Age of Intelligent Health Systems.

Tom is one of the most respected voices on artificial intelligence and health information. My own book, Healing the Sick Care System: Why People Matter, another bestseller, looks at health care through the lives of patients, families and clinicians navigating a system that can be brilliant in moments and bewildering in motion.

Two Books, One Shared Concern

Our books were already in conversation before we arrived at the Summit. Tom wrote the Foreword to Healing the Sick Care System: Why People Matter, and I wrote the back-of-book review for Health Care Nation. We had each recognized the connection between the two. Readers, however, often encounter books separately. One may see Tom’s as a system-level call to redesign health care and mine as a people-first call to restore humanity to care. On stage, with an audience ready for the discussion, the relationship became easier to feel and internalize.

One book shares why the system must change. The other asks who that change must serve. Together, they return health innovation to the question that should guide every decision: whose life is made better because we invent?

That question was at the heart of Amir Lahav’s DHAI Summit. Curated by Amir Lahav, PhD, the Summit brings together people across artificial intelligence, digital health, health systems, research, investment and care delivery, from industry leaders such as Amazon Web Services, represented at the Summit by its Global Chief Medical Officer, Rowland Illing, MD, to trade groups like the Decentralized Trials Research Alliance championed by Craig Lipset, co-chair, and companies such as MassiveBio, an AI-powered platform to match patients to 19,000+ oncology and hematology trials worldwide, represented by Arturo Loaiza Bonilla, MD MSEd, are harnessing information to advance science and save lives.

Lahav’s achievement is far more than assembling experts. He has created a setting where different parts of the health community can bench-test their thinking against one another.  The action on the mainstage spills over to the hallways and receptions. That matters because AI in health cannot mature within a single discipline. Data scientists need clinicians. Clinicians need workflow support. Innovators need patient insight. Investors need to understand adoption. Health system leaders need to know when technology solves a problem and when it adds another layer of friction to an already complex ecosystem.

The audience brought energy to the room because the topic was more than technical. People wanted to talk about what AI makes possible. They wanted to talk about what health care cannot afford to forget. Health has become transactional. The operational aspects of care carry more friction than compassion. Patients are asked to coordinate their care across portals, referrals, insurance prior authorizations, clinical handoffs, and delayed communication. Clinicians are asked to heal while absorbing new layers of documentation, digital alerts, measurement and workflow pressure.

That is the context in which AI enters health care.

AI is curated knowledge and amplified pattern recognition. It can search for information no person could hold alone. It can surface signals, compare data, support decisions and make complexity more manageable. Used well, it can help clinicians, researchers, health systems and patients see what might otherwise remain hidden.

Harvey Castro, MD, MBA, a physician futurist and AI health-care innovator, understood that connection. He shared that he was heading to Portugal to speak on AI and health care, and that he would be reading Healing the Sick Care System during this flight. His encouragement reflected what made the Summit meaningful. The conversation was not ending with our fireside chat. It was traveling with people who are carrying the future of health AI into new rooms, new audiences and new decisions.

Insight, however, is different from wisdom. A pattern is different from a person. A recommendation is different from a relationship. AI can help reveal possibilities. People must decide how those possibilities impact the realities of illness, fear, family, access, culture and care.

That is where Tom’s work and mine meet. Health Care Nation asks why a country with extraordinary science, clinical talent, and technology continues to struggle with fragmentation, costs, incentives, and uneven access. Tom challenges the habit of waiting for someone else to fix what is broken. Policymakers, executives, payers, providers, employers, innovators and citizens all shape health care through choices, incentives, habits and expectations.

Healing the Sick Care System: Why People Matter starts from the same concern through the experience of a person seeking and delivering care. It asks what happens when a system with remarkable capabilities becomes so difficult to navigate that professional burnout leads to abdication, shifting more of the confusion, delay and uncertainty onto the very people seeking care. It looks at what care feels like when people seek treatment yet still feel lost, when they meet skilled professionals yet leave without understanding the next step, and when they are surrounded by technology yet feel lost and alone.

When Innovation Forgets the Person

Health care does not lack brilliance. It has extraordinary science, dedicated professionals, ambitious innovators and vast resources. Yet brilliance loses force and investment loses meaning when the system becomes more focused on transactions than on the people seeking care.

Soner Haci, CEO of PONS, captured that spirit after the session, writing that the story Tom and I shared was exactly why PONS was founded. His response mattered because it connected the fireside conversation to entrepreneurial purpose. Strong health companies often begin with the recognition that a problem people have learned to work around should no longer continue.

That is also why Lahav’s careful curation mattered. The Summit gave innovators a place to discuss more than what can be built. It invited people to consider whether what is being built is useful, human and ready for the realities of care. In health, possibility is never enough. The measure is whether the possibility improves the experience of the person seeking care and the person trying to provide it.

Tom is especially conscious of how many health professionals experience new technology. AI may be introduced as an aid, yet it can feel like another responsibility added to an already strained workflow. When a tool requires more clicks, more documentation, more review or more mental switching, it becomes one more demand on the people it was meant to support.

That concern should be central to the AI conversation. Implementation matters as much as innovation. AI earns trust when it reduces burden, fits the rhythm of care and gives clinicians back time for judgment, conversation and healing. A tool that adds work, noise or uncertainty to care has missed the purpose of health innovation.

Leanne West, innovation catalyst, patient advocate, connector, Chief Engineer of Pediatric Technology at Georgia Tech, and President of the International Children’s Advisory Network, reflected on LinkedIn that the fireside discussion was “speaking my language.” She highlighted a line from Healing the Sick Care System, that doctors should be people first and doctors second. Her reaction captured why the discussion resonated. The audience heard an AI conversation that kept returning to people.

That return to people is not sentimental. It is central to the challenge. People navigating illness often understand system failure with painful precision. They know where the instructions were confusing, where the portal failed, where follow-up disappeared, where a handoff became a gap and where no one seemed accountable for the whole experience.

Communication belongs in the same conversation. In health care, silence changes the experience. Confusing instructions, disconnected portals, delayed follow-up, fragmented records and unanswered questions become part of how people remember care. AI and digital health can help by making communication more useful, timely, and understandable. The goal is better understanding, not more automated volume.

Prevention also belongs in the same conversation. Health Care Nation argues that the health of people and the nation are inseparable. A country cannot continue spending enormous resources on illness while underinvesting in what helps people stay well. Healing the Sick Care System reaches that point through the patient’s experience. People should be seen, supported and guided before their physical and mental health reaches the snapping point.

This is the power of DHAI. Amir Lahav created a space where AI was discussed in the context of health’s larger obligation. Lahav even hosted a panel on pediatrics, where adults and children as young as six sat together on the mainstage, offering counsel. The conversation was not limited to algorithms, platforms or market opportunity. It asked whether innovation can reduce friction, protect health professionals, support patients, strengthen communication and make care more human.

Those are the questions that move AI from novelty to value. Can it help identify risk earlier? Can it make information easier to understand? Can it reduce administrative burden? Can it help match people to appropriate care? Can it support better conversations? Can it give clinicians back time to listen, think and guide? Can it help people feel less alone, less confused and more supported?

Together, Tom’s book and mine point toward priorities that health leaders should keep close: build around people, invest in prevention, reduce friction, protect clinicians, align incentives, listen to patients, measure outcomes and use technology wisely.

AI as Insight, Not Replacement

AI will not repair a fragmented system on its own. If incentives remain misaligned, AI may optimize the wrong outcomes. If patients remain peripheral, AI may scale impersonal care. If communication remains broken, AI may create more messages without creating more meaning. If trust is treated as an assumption, people will resist new tools for understandable reasons. This is why people absolutely matter.

The future worth building is hopeful. AI can help us see patterns earlier, connect knowledge faster and support better decisions. It can help researchers, clinicians and health systems work with greater insight. It can help people move through care with less confusion and more support. Its value grows when insight is joined with human judgment.

That was the heart of our fireside conversation, and that was why the audience response was powerful. We are not lacking ideas. We are not lacking innovation. We risk allowing health care to become ever more transactional at the very moment when technology should help us make it more connected, understandable and humane.

In Health Care Nation, Tom Lawry reminds us that we must stop waiting for someone else to fix the system. Healing the Sick Care System reminds us that every improvement must be judged by the lives of the people seeking care and the people providing it. These are companion calls to action.

“The future is calling,” as Tom writes. It may indeed be better than we think. It will become better when insight is joined with empathy, when innovation is guided by purpose and when the people touched by health-care systems shape what comes next.

AI can help us see more. People must decide what to do with what they see.

The next chapter belongs to us.

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It goes without saying that artificial intelligence has digitalized everything these days, including the healthcare sector—ranging from mental health chatbots to health assessment and monitoring tools. While these tools are impressive in terms of quality and speed, many users may abandon them after initial use. Alternatively, there may be a lack of sufficient trust in user data privacy, and this inability to capture consumer trust can leave product developers disheartened. These are just a small fraction of the challenges and issues that the digital health sector faces today.

These concerns are not merely theoretical assumptions. Recent digital health industry reports show that sustaining user engagement and retention remains one of the most critical challenges in this field. Despite heavy investments in developing technical and AI capabilities, many digital health startups face high drop-off rates and declining user engagement after the first few weeks of use. Recent reports from Rock Health and Deloitte have also shown that trust, user experience, and user-perceived value are among the most critical factors determining the success or failure of digital health solutions.

Let us dissect the core challenge a bit more deeply.

The reality is that the ultimate success of digital health tools does not depend solely on their technical prowess, even though the most precise mathematical calculations are designed and implemented by elite engineering teams to build these tools. Rather, their ultimate success depends on the quality of the user’s “cognitive,” “motivational,” and “behavioral” experience. In other words, the core issue is not simply “what the AI knows” and how fast it delivers it to users; the golden nugget is “how the human interacts with the AI.”

In the previous article or part one, the importance of “learning sciences” in developing “human-centered AI” in digital health was discussed. I highlighted the crucial point that the missing link in AI technology, including digital health, is the absence of a vital foundation known as the learning sciences. The present article is an operational continuation of that discussion, attempting to demonstrate how the learning sciences can serve as a framework for designing cognitive and behavioral experiences in AI-driven digital health tools.

In this article, I offer recommendations that are more operational in nature for manufacturers and designers of AI tools within the digital health industry.

Learning Sciences as the Foundation for AI Design in Digital Health

The learning sciences and psychology of learning consist of a body of findings and theories regarding how a relatively permanent change occurs within an organism or learner. These changes depend on various internal and external factors. Furthermore, this change involves the learner’s cognitive, behavioral, motivational, and physiological dimensions.

With this simple description, it becomes clear that the learning sciences are not limited strictly to educational environments. Because they study the processes of cognition, attention, motivation, mental engagement, feedback, self-regulation, and behavior change in human-environment interaction, the learning sciences are vital wherever learning, interaction, action and reaction, or behavioral continuity are involved. They aid us in understanding the behaviors, motivations, cognitions, and perceptions of learners.

Especially in the era of AI, the science and psychology of learning demand deeper immersion and greater precision in constructing AI tools. This urgency arises from growing concerns that these tools may not be human-centered, neglecting the existential dimensions of the human being as the primary user.

One of the most important areas of AI application is digital health. The learning sciences can serve as a necessary prerequisite in AI design, acting as both an interpreter and a facilitator.

In what ways are they a prerequisite for AI and digital health?

They are a prerequisite because digital health tools can themselves be viewed as environments for learning and behavior change—environments where users are constantly interpreting information, making decisions, regulating behavior, and building trust. Many current digital health tools, despite their technical complexity, are not designed based on the cognitive, behavioral, motivational, and transactional complexities of human beings.

Some users have reported that the explanations provided by AI systems were ambiguous, complex, or even confusing to them. This finding aligns with the World Health Organization (WHO) report on the ethics and governance of artificial intelligence for health. The report emphasizes that explainability is only valuable when it is understandable to the end-user, as overly complex explanations can themselves become a factor in reducing trust and increasing confusion.

In certain studies, users have stated that they cannot comprehend the system’s decision-making logic and are forced to simply trust or distrust its output blindly. In many cases, the user eventually abandons these tools after a while—a reaction that a learner might similarly display in an AI-driven educational environment!

These and similar problems demonstrate that providing information clearly and orderly is not enough on its own. The interaction must be meaningful and comprehensible within the user’s cognitive dimension; the tool must understand the user’s behavior and reinforce their motivations. As a result, continuous, purposeful interaction and trust will be fostered. It is precisely through purposeful interaction and trust that the tool becomes useful and works in service of the consumer (or the learner).

Designing Tools Aligned with the User’s Cognitive Dimension

As previously stated, the human being is a creature of interwoven, complex dimensions. According to psychological and especially learning theories, a major part of the learning process occurs mentally within the dimension of cognition. Therefore, understanding the formula of learning and its cognitive dimension is an essential blueprint for designing digital tools.

For instance, in the human learning process, an unfamiliar and unknown topic transitions into a familiar one through distinct stages. This process can typically occur via mental stimulation and environmental support. If this learning is deep and meaningful (rather than superficial or based on rote, parrot-like memorization), it can be recalled for a long time, and the likelihood of forgetting is minimized.

The science of learning encompasses cognitive theories that emphasize concepts such as perception, meaningfulness, the integrated whole, problem-solving, scaffolding, and similar ideas. If designers implement these abstract concepts operationally, they can achieve practical results, including solving the following issues:

1. Information Overload: One of the most significant challenges in digital health tools is information overload. For example, in some studies conducted on chronic disease monitoring platforms, users reported that a high volume of simultaneous notifications, charts, and recommendations led to mental fatigue and a decreased willingness to continue usage. Researchers describe this phenomenon as a type of Cognitive Overload, which can even degrade the quality of health decision-making. Users often interact with these tools under conditions of stress, anxiety, or mental exhaustion. In such states, presenting a massive volume of data, alerts, or advice simultaneously can induce cognitive fatigue, confusion, and reduced decision-making quality.
2. Complex Explanations: In some research, users of AI-driven health systems reported that overly complex explanations did not increase their trust; instead, they heightened anxiety, hesitation, and mental strain. These findings demonstrate that successful cognitive design does not mean providing more information, but rather reducing mental strain and facilitating user comprehension.
3. Sustaining Attention: Maintaining users’ attention and mental engagement is another major challenge in digital health. Many health applications are abandoned by users after a short period. Reviews published in the mHealth sector show that a significant portion of health app users severely reduce or entirely stop their interaction within the first three months. This indicates that initially acquiring a user and retaining their cognitive engagement for continuous use are two completely different challenges. Part of this issue stems from the interaction experience becoming repetitive, impersonal, and cognitively tedious. The missing goal here is the personalization of the process!
4. Gradual Adaptation: On the other hand, altering a user’s attitude and perception is typically a gradual process, not an instantaneous one. Yet, some digital health tools deliver a large volume of recommendations and information abruptly, without accounting for the user’s gradual learning and adaptation process. Learning sciences can help design experiences that create progressive, sustainable paths for shifting attitudes and beliefs toward a process or a tool, rather than applying sudden pressure.

Designing Tools with Regard to the Users’ Behavioral Dimension

As noted earlier, learning involves permanent changes in behavioral potential. Therefore, if a change occurs in the users’ cognition and attitude, we expect to see corresponding changes in their behavioral performance as well.

The learning sciences introduce frameworks to help us understand when and how a behavior becomes consolidated. How, and under what conditions, can we successfully navigate the channel of user cognition, establish a positive attitude toward using a tool or smart test, and ultimately compel them toward stable, purposeful behavior?

The psychology of behavior, as a branch of the learning sciences, steps in at this stage to assist digital tool designers. It prescribes that the formation and continuity of behavioral learning follow specific stages. Therefore, you must clearly define the behavioral prescription you intend to instill in the user:

For instance, in many diabetes management or weight loss programs, merely presenting information about an individual’s health status has not led to behavior change. Studies have shown that when tools provide features for gradual goal-setting, self-monitoring, and continuous feedback, the probability of forming sustainable health behaviors increases.

In short, determine what the target behavior is and define it clearly. Through what stages and micro-steps does this behavior form? What types of feedback and responses guide the user toward the final target behavior? Once the target behavior is formed, what factors or feedback mechanisms can sustain it? And based on what metrics can we determine that the user’s behavior and its continuity result from the proper functioning of the tool?

Furthermore, because our objective is to build human-centered AI and tools, we do not intend to control the user. Instead, we aim to reinforce healthy attitudes and behaviors by boosting their sense of self-efficacy and perceived control over their health journey. In doing so, we guide their behavior and choices along the right path, aligned with the human blueprint.

In fact, one of the growing concerns in the literature on AI in healthcare is the reduction of Human Agency. Some experts have warned that if systems replace human decision-making rather than enhancing it, cognitive dependency and diminished independent judgment may lead to unintended consequences. Hence, the goal of human-centered design must be user empowerment, not user replacement.

Additionally, creating sustainable behavioral habits requires progressive interaction, continuous feedback, and a design that adapts to the real-world context of users’ lives. Tools designed without considering the cognitive and social conditions of the user frequently fail to yield lasting change. Understanding behavioral science and the factors influencing the reinforcement or weakening of a response helps designers correctly guide user behavior while identifying and controlling potential confounding variables inherent in digital tools.

Designing Tools Aligned with the User’s Motivational Dimension

Precisely when some neuroscience specialists argue that everything occurs at the level of cognition and that all other complex human aspects are overshadowed by it, the learning sciences (of which neuroscience is only a part) tell us it is not that simple!!! Learning an idea is a process. If we want a meaningful idea—such as using a health monitoring app—to transform into a highly repetitive, sustained behavior, we must account for other human dimensions as well!

“Trust” is one of the most critical factors in the adoption and motivational continuity of digital health tools. However, trust is not built solely through technical transparency. Users need to feel that the system is understandable, predictable, and psychologically safe.

Several studies have shown that complex or overly technical explanations fail to build trust and instead trigger greater anxiety and confusion among users. Moreover, concerns regarding privacy, data sharing, and the secondary use of health data represent major drivers of distrust among users. In digital health, trust is not merely a technical issue; it is part of the relational experience between the human and the system. For this reason, user experience design must consider psychological safety and relational trust alongside technical security.

Why is trust important? Because it is the loop that connects a user’s cognition, beliefs, and attitudes to their actual behavior! It generates the necessary motivation for follow-through, and ultimately, consolidates a behavior.

Findings from studies conducted on mental health chatbots indicate that anxiety over how personal data is stored, secondary data use, and a lack of transparency regarding data ownership are primary factors driving down user trust. In many instances, users evaluated the perceived quality of the relationship with the system as even more critical than the technical complexity of the algorithm.

While many digital health tools focus heavily on delivering information, possessing information does not automatically translate into the “motivation” required for behavior change. If the user does not feel capable of performing the recommended behavior, the likelihood of continued system utilization drops.

Alongside trust as a motivational component of user behavior, one of the most foundational concepts in the psychology of learning is “self-efficacy.”

Extensive research in health behavior change demonstrates that individuals who believe they possess the capacity to execute recommended actions are far more likely to initiate and maintain the new behavior. Consequently, successful design does not stop at giving advice; it must craft an experience where the user can taste small but meaningful victories.

Self-efficacy refers to an individual’s belief and confidence in their own abilities to organize and execute the courses of action required to achieve a specific goal. This psychological attribute can be modulated via controllable, situational feedback, and AI designers can leverage it as a key lever to impact human motivation.

In a digital health environment, this motivational characteristic can serve as the driving force behind consumer behavior when interacting with smart medical tools. Therefore, AI-driven tools must be capable of reinforcing a sense of empowerment and progressive mastery in the user, rather than merely broadcasting a barrage of alerts and directives. Studies indicate that users achieve more sustainable, satisfying engagement with health systems when feedbacks are personalized, actionable, and contextualized within the actual reality of their daily lives.

Feedback itself is only effective when it is timely, clear, and meaningful. Generic, non-actionable feedback—such as vague lifestyle advice—typically exerts a highly limited impact on behavior change. Conversely, contextualized, action-oriented feedback can significantly heighten both the cognitive and motivational engagement of the user.

Expected Operational Implications for Design and Development Teams

Many digital health tools still focus predominantly on algorithmic performance and technical functionalities, whereas the sustainability of human-system interaction relies on the quality of the cognitive and behavioral experience—and, of course, the motivational loop that links cognition to behavior and drives its continuity.

The learning sciences and psychology of learning can empower design and development teams to move far beyond the mere metrics of “ease of use” and “time management.” This shift is the most vital achievement of a cognitive, motivational, and behavioral architecture governing human-AI interaction.

This issue holds particular urgency for startups, product design teams, and digital health developers; the true success of these tools does not hinge on the sheer number of features, but on their capacity to preserve sustained engagement, secure trust, and guide human behavior change.

The learning sciences offer a framework to design tools that are not just usable, but comprehensible and justifiable within the users’ cognitive schemas. It shapes and directs user behavior under their own autonomy through self-regulation mechanisms, supplies the motivational loops connecting thought to action, and stabilizes user behavior.

Perhaps the most definitive question for the future of digital health is not how much smarter artificial intelligence will become, but rather how much better it can comprehend human cognition, motivation, agency, and behavior. Ultimately, the most successful tools will not necessarily feature the most advanced algorithms, but those that possess the deepest understanding of the human being.

References

Bandura, A. (1997). Self-Efficacy: The Exercise of Control. New York: W.H. Freeman.

Schunk, D. H. (2020). Learning Theories: An Educational Perspective (8th ed.). Pearson.

Hergenhahn, B. R., & Olson, M. H. (2015). Theories of Learning (7th ed.). Pearson.

Zimmerman, B. J. (2002). “Becoming a Self-Regulated Learner: An Overview.” Theory Into Practice, 41(2), 64-70.

Deloitte. (2024). 2024 Global Health Care Outlook.

Deloitte Center for Health Solutions. (2024). Digital Transformation and Consumer Engagement in Healthcare.

Rock Health. (2024). Digital Health Consumer Adoption Survey.

Blease, C., Kaptchuk, T. J., Bernstein, M. H., et al. (2019). “Artificial Intelligence and the Future of Primary Care.” The Lancet Digital Health, 1(8), e353-e354.

Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.

World Health Organization (WHO). (2021). Ethics and Governance of Artificial Intelligence for Health.

Sweller, J. (1988). “Cognitive Load During Problem Solving: Effects on Learning.” Cognitive Science, 12(2), 257-285.

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At a time when Artificial Intelligence is rapidly weaving itself into the fabric of physical and mental healthcare, basic user-friendliness, processing speed, and market acceleration are no longer enough. To build digital solutions that actually work, we must grasp how humans learn, adapt, and transform their behaviors. This is exactly where the learning sciences become vital. Put simply, until we decode the mechanisms of ‘deep learning in humans’ through the lens of learning sciences, the concept of ‘Deep Learning’ in AI development will never reach its true potential.

Digital health, much like any other modern domain, is now permanently tied to technology. From education and corporate structures to parenting and economics, technology is built to streamline processes, widen access, and boost precision. At its core, technology was created to serve humanity across individual and social spheres, and digital health stands as one of the most critical testing grounds for this promise.

Yet, alongside this reality lies a much bigger issue—one that is gaining traction and deserves a rigorous, interdisciplinary look.

The question isn’t whether technology is inherently good or bad; it is that even the most advanced technology remains ineffective if it fails to align with human blueprints.

Today, more than ever, we need to look at AI and digital systems through a deeply human lens. This means moving away from treating an individual merely as a ‘user,’ a ‘data processor,’ or a passive ‘receiver,’ and instead recognizing them as a multi-dimensional, complex, living being.

In digital health, our core focus is the human being—the patient striving for recovery, the client seeking a precise diagnosis, the therapist requiring sharper diagnostic tools, or the physician leaning on technology to make high-stakes clinical decisions. The human is always the ultimate destination. If a digital tool is to succeed in this space, it must genuinely connect with real people, accounting for their cognitive, behavioral, biological, and experiential complexities.

Why Research in the Learning Sciences is Indispensable

In the digital health space, the real challenge is never just about getting someone to install an app or use a digital tool temporarily. The true measure of success is whether that tool can drive a real, lasting change in human behavior, attitude, and lifestyle. If a person engages with a platform for a brief period but experiences no sustainable shift in their health or daily habits, the technology has fundamentally missed its mark.

This is where the learning sciences help us elevate technology design far beyond surface-level mechanics and computational algorithms. When we understand how a person actually internalizes information, we can build better communication strategies, deliver more constructive feedback, apply the right behavioral reinforcements, and create environments that foster genuine trust, motivation, and user engagement.

Furthermore, this scientific backing allows us to grasp privacy and data security from the psychological standpoint of the user, since a patient’s willingness to trust a system is directly tied to how safe they feel sharing their data.

Two Foundational Pillars: Trust and Continuance Intention

To see how the learning sciences practically guide human behavior in the era of AI, we can look at two crucial dynamics in digital health:

1. The Mechanics of Trust
Trust is the ultimate currency in digital health, because users are asked to hand over highly sensitive personal, biological, and psychological data to an algorithm.

2. Continuance Intention and Habit Formation
Capturing a user’s attention at launch is relatively easy; keeping them engaged over time is where the tech industry routinely struggles.

Conclusion

The defining critique of modern AI is not its widespread adoption, but its lack of authentic human-centricity. Successful digitalization in healthcare cannot rely solely on technical scalability; it must place the complex human being squarely at the center of the design process.

Technology only gains meaning when it can understand human beings, build a relationship with them, earn their trust, and guide them toward lasting well-being.

Ultimately, the future of digital health will not be measured by raw processing power, but by the depth of the developer’s understanding of the human condition.

References

Sucala, M., Cole-Lewis, H., Arigo, D., Oser, M., Goldstein, S., Hekler, E. B., & Diefenbach, M. A. (2021). Behavior science in the evolving world of digital health: Considerations on anticipated opportunities and challenges. Translational Behavioral Medicine, 11(2), 495–503. https://doi.org/10.1093/tbm/ibaa034

Bai, B., & Guo, Z. (2022). Understanding users’ continuance usage behavior towards digital health information system driven by the digital revolution under COVID-19 context: An extended UTAUT model. Psychology Research and Behavior Management, 15, 2831–2842. https://doi.org/10.2147/PRBM.S364275

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