Between those meetings sit two practical questions that diplomacy cannot settle on its own. What equipment will actually cut emissions, and who will pay to install it? Implementation runs through power grids, factories, data centers, farms and supply chains. Each one already operates at an enormous scale, and changing how it works means replacing or upgrading physical machinery, often one site at a time.

An industrial problem

“Climate is an industrial problem,” says Elliott Parker, CEO of Alloy Partners, a firm that co-creates startups with large corporations. “The technologies that matter most have to work inside energy systems, manufacturing, infrastructure and supply chains that are already enormous.”

That description fits the sectors where emissions are hardest to cut. Steel, cement, chemicals and heavy transport need new processes and materials, not only cleaner electricity. A breakthrough in a lab counts for little until it runs reliably inside a plant that cannot afford downtime.

AI has changed where the money goes

The rapid growth of AI has added new pressure. Data centers need large amounts of power and cooling, and investors have followed that demand. TechCrunch, citing PitchBook data, reported that climate tech venture deal value topped $14 billion in the first quarter of 2026, with most of it in sectors boosted by data center construction, including the built environment, grid infrastructure and dispatchable energy.

Some investors see a trade-off. “Decarb is dead for now,” one venture capitalist told The Wall Street Journal last month. The climate impact of the AI buildout will depend largely on whether new demand is met by low-carbon or fossil-fuel generation.

Resistance is also coming from the communities that host the buildings. Wired reported last month that opposition to data centers in the United States is driven less by global climate worries than by local ones, such as water use, rising electricity bills and pollution. Climate Week NYC itself saw activists interrupt a panel on AI and data centers. For the industry, that makes efficiency a condition for getting projects approved, alongside its value for the climate.

The two goals can overlap. Technologies that cool servers with less energy and water, recover waste heat or help grids absorb new load serve AI growth and emissions reduction at the same time. Those are the categories where hardware startups can find paying customers now.

What scarcity teaches

Countries that have long operated with limited water, land or energy offer a useful view of how resource technology reaches the market. Israel is one example. Modern drip irrigation was developed and commercialized there, and the country now relies on large-scale desalination for much of its drinking water.

That history continues in today’s startups. According to PLANETech, Israel’s climate technology community, more than 1,500 active startups in the country are working on industrial and climate challenges. Research institutions such as the Technion and the Weizmann Institute of Science supply engineering, chemistry and physics talent.

Kerem Nevo, VP and Head of the Growth Division at the Israel Innovation Authority, highlights the links between research and industry. “Our highly interconnected innovation ecosystem has a proven ability to bridge academia and industry, transforming cutting-edge research into commercially viable technologies,” he says.

Global companies and investors already treat Israel as a place to build. According to the Israel Innovation Authority’s 2026 High-Tech Status Report, 511 multinational companies operate in the country. Israeli tech companies raised about $14.6 billion in 2025, up 30% from 2024, and foreign investors accounted for 70% of that venture capital.

Bringing buyers to the technology

Deepening those ties is the purpose of PLANETech Week, which runs October 18-22, featuring nearly 30 events and an expected thousands of attendees. International investors, corporate leaders and delegations will meet Israeli startups working on waste, critical minerals, construction, sustainable aviation fuels, data centers, energy and mobility. Partners include Siemens Energy, Microsoft, Ormat New Ventures and Volkswagen Group’s Konnect innovation hub, alongside the Israel Innovation Authority.

The week’s flagship event, PLANET Connects, takes place October 21 at the Einav Cultural Center in Tel Aviv, with a startup exhibition, a B2B meeting area and a main-stage plenary with international speakers.

“PLANETech Week is designed to bring this ecosystem to the international conversation on climate action and put it in front of the investors and industrial partners who can deploy it at scale,” says Rotem Trivizki, Director of PLANETech.

Companies to watch this week

Several of the companies taking part in PLANETech Week are working on the challenges described above. Their solutions draw on materials science, physics, and chemistry, and are built into technology that has to perform at an industrial scale.

• NanoTherma is developing thin thermoelectric panels built on nanomaterials that capture low-temperature waste heat inside server racks and convert it into electricity.
• IDE Technologies, the Israeli company behind some of the world’s largest desalination plants, offers high-recovery treatment that recycles water from data center cooling towers.
• BH Grid Solutions tracks a data center’s carbon emissions hour by hour and helps operators and tenants shift computing work to cleaner hours and locations.
• Starstone makes a biogenic, zero-carbon cement that reduces reliance on the high-temperature kilns used in traditional cement manufacturing.

The measure that matters

COP31 will produce negotiated text, and climate weeks will produce announcements. The harder test is how many pilots turn into purchase orders, and how many purchase orders become equipment running inside grids, plants and data centers.

That work happens at events like PLANETech Week, in meetings between engineers who have built something and companies with the budget and sites to deploy it. The world’s climate goals will be met or missed in thousands of those decisions.

The post Meeting Climate Goals Will Depend on the Machines That Run Industry appeared first on Medika Life.

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

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

Lawry Combines Leadership with Caring

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

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

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

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

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

From a Promising Pilot to Better Care

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

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

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

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

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

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

From Applications to Expectations

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

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

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

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

Available Now on Amazon

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

The post Tom Lawry’s AI in Health Asks the Question Leaders Need to Answer appeared first on Medika Life.

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

What happens before you arrive

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

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

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

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

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

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

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

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

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

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

That lesson belongs at the center of health innovation.

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

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

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

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

From Fragmentation to Friction

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

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

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

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

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

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

Communication is a Key Implementation Strategy

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

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

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

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

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

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

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

Designing Innovation for the Real World Experience

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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The body remains, but the self doesn’t.

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

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

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

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

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

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

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

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

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

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

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

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

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The Exhibition Floor as a Market Signal

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

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

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

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

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

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

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

Health-Specific AI Moves from Possibility to Practice

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Future Belongs to Reliable Outcomes

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

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

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

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

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

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

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

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

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

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

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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.

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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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