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

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

Lawry Combines Leadership with Caring

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

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

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

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

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

From a Promising Pilot to Better Care

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

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

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

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

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

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

From Applications to Expectations

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

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

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

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

Available Now on Amazon

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

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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.

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.

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

The post Human-Centered AI in Digital Health: Why Learning Sciences Matter appeared first on Medika Life.

Early in the book, Nosta writes, “The solved can never touch the whole.”  That line lingers. It captures the essence of his argument. AI can solve, generate, synthesize, and accelerate. Yet something about the human experience of thinking, the struggle, the friction, the meaning-making, exists beyond resolution.

This tension defines the book. It is not anti-technology. Nosta is deeply engaged with AI and candid about its value. He describes large language models as tools that “move faster and connect more disparate concepts than our minds could ever manage on their own.”  He is equally clear that this capability introduces a subtle risk. We may begin to outsource not just tasks, but thought itself.

That distinction matters more than many may be willing to admit.

From Tools to Thought

One of the most compelling contributions of The Borrowed Mind is its framing of AI not as the next step in computing, but as a turning point in cognition. Nosta traces a clear arc. Gutenberg unlocked words. Google unlocked facts. AI, he argues, is unlocking thought. 

That progression is elegant, yet also unsettling. Words and facts could be externalized without fundamentally altering the structure of human reasoning. Thought is different. It is intimate. It is identity. It is how we become.

Nosta reminds us that thinking once required effort, a type of natural friction that created sparks of innovation. “The distance between question and answer created space for our discernment.”  Within that space, judgment formed, curiosity deepened, and understanding took root.

AI compresses that distance. It removes friction. It delivers coherence with remarkable speed.  One of the book’s most important insights emerges here. Coherence is not the same as understanding.

Nosta introduces the concept of “anti-intelligence,” describing it as “fluency without understanding. Coherence without experience.”  AI does not think. It mirrors the structure of thinking. It produces language that resembles reasoning without sharing its origin.

In health, where evidence, interpretation, and judgment must coexist, this distinction is not academic. It is operational. It shapes how clinicians trust tools, how leaders deploy them, and how patients ultimately experience care.

The Seduction of the Socratic Mirror

One of the most original sections of the book is Nosta’s description of the “Socratic Mirror.” He draws a parallel between classical dialogue and modern AI interaction. Socrates asked questions to surface the truth. AI, in a different way, reflects our thinking back to us, reframed, extended and sometimes clarified.

Nosta writes that the model “…does not tell me what to think but creates the conditions under which my own thinking could deepen.” This is where the book moves beyond critique and into possibility.

Used well, AI becomes a cognitive partner. It expands perspective, accelerates exploration, and invites iteration. In clinical research, patient engagement, and system design, this capacity holds enormous promise.

Nosta does not romanticize the relationship. He recognizes its asymmetry. The model has no interior life. It does not ponder. It does not carry consequence. It does not bear responsibility. That responsibility remains human.

Rethinking the Fear of Displacement

A persistent anxiety runs beneath every conversation about AI. Many fear it will become a job slayer, a force that displaces rather than elevates human contribution. That concern is understandable, yet not new.

Every meaningful advance in technology has reshaped how people work. The wheel did not eliminate labor. It redefined movement. The stethoscope did not replace physicians. It extended their ability to listen and interpret. The tollbooth transponder did not end transportation roles. It changed the flow and focus of human involvement. Each innovation shifted roles, demanded new skills, and expanded what people could do.  AI belongs in that lineage.

What distinguishes this moment is not the elimination of work, but the redistribution of cognitive effort. The real risk is not that machines will think for us, but that people may become less inclined to think for themselves. Nosta’s warning is subtle yet profound. Surrendering curiosity, judgment, and reflection to systems that generate answers with ease risks dulling the very faculties that define human intelligence.

This is why The Borrowed Mind is such an important read at this moment. It does not dismiss concerns around job displacement. It reframes it. The central challenge is not protecting roles as they exist today, but strengthening the uniquely human capacities no system can replicate. Creativity, discernment, ethical reasoning, and the ability to navigate ambiguity are not diminished by AI. They become more essential.

The book offers reassurance without complacency. The future of work will favor those who sharpen their thinking, engage deeply with ideas, and remain active participants in their own intellectual development. The machine is not the adversary. Neglecting the development of one’s own mind is a danger.

Composite Intelligence and the Limits of the Machine

Nosta introduces “composite intelligence” to describe the interaction between human and machine cognition. Composite does not mean blended into sameness. It means distinct contributions working in concert. The model brings speed and breadth. The human brings depth.

This triad becomes one of the most useful frameworks in this book. AI excels in velocity and scale. Depth, the slow transformation of understanding, remains human. As Nosta writes, “Models do not ponder.” 

In health, this distinction is profound. Data can inform. Algorithms can suggest. The act of deciding, especially in moments of uncertainty, requires something more. It requires what Nosta elevates as the defining human contribution. Virtue.

Drawing on Aristotle’s concept of practical wisdom, Nosta reminds us that judgment is forged through experience, consequence, and accountability. A model can generate options. It cannot live with outcomes.

This is where the book resonates most deeply for those working in health. Intelligence is becoming abundant. Discernment is becoming scarce and, therefore, more valuable.

The Risk of the Borrowed Mind

The book’s title is not metaphorical. It is a warning. Nosta argues that as engagement with AI deepens, internal dialogue begins to change. The model becomes a cognitive tuning fork, subtly shaping how questions are framed, how ideas are explored, and how answers are anticipated. This dynamic is not inherently negative. It can elevate thinking, accelerate learning, and make complex domains more accessible. Dependency remains the concern.

Reliance on generated thought risks weakening the muscle of original thinking. Access can be mistaken for understanding. Individuals may become, in Nosta’s words, “cognitive clones.” 

This concern is particularly relevant in health ecosystems already strained by time, complexity, and administrative burden. The temptation to offload cognitive work will be strong. The discipline to remain intellectually engaged will be essential.

A Book About AI That Is Not About AI

What makes The Borrowed Mind stand apart is that it is not ultimately about technology. It is about humanity. Nosta writes, “This book is not really about technology. It is about you.”  That idea anchors this work.

Readers are challenged to consider what it means to remain “the authors of our own minds.”  Not passive recipients of generated insight, but active participants in meaning-making.

This question sits at the center of the health ecosystem’s future. As AI becomes embedded in clinical workflows, research, and patient engagement, the issue is not whether it will improve efficiency. It will.

The deeper question is whether it will deepen humanity or dilute it. Will it create space for clinicians to think more deeply, connect more meaningfully, and act more wisely? Or will it create a system that values speed over reflection, output over understanding, and coherence over truth?

Nosta offers no simple answers. He offers a framework for asking better questions.

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Over 987 million people around the world now use AI chatbots regularly. Research shows that nearly half of Americans with ongoing mental health conditions have turned to a chatbot for emotional support in the past year alone. That’s a huge number of people relying on a technology that’s still very new in mental health care. So what does this mean?

Is it a big step forward in making help more accessible, or are we taking a risky chance? As with most things, the truth is somewhere in the middle. These tools offer real benefits, but they also come with real risks. It’s important to look at both sides honestly.

The Case for AI Chatbots in Mental Health

First, let’s look at why so many people are turning to these tools. There’s a mental health crisis, and not enough providers to help everyone who needs it. Long wait lists, high costs, and the ongoing stigma around seeking help all make it harder for people to get care. For someone who can’t afford therapy, can’t find an available provider, or feels too embarrassed to talk to someone in person, a chatbot that’s always available can feel like a lifeline.
Research supports this to some extent. Corporations are responding to this, and more TV ads are appearing that offer online therapy with or without chatbots.

A systematic review of 31 randomized controlled trials, which is considered the gold standard in research, found that AI chatbots helped reduce anxiety and depression symptoms in adolescents and young adults. Another meta-analysis of 14 strong trials found a clear positive effect on mental health outcomes, showing these tools are more than just placebos. For college students, who often face unique pressures and may avoid formal help, chatbots have shown promise in building coping skills and improving emotional well-being.

Anonymity is important, too. People are more likely to open up when they don’t feel judged. Studies show that users see the chatbot’s lack of social expectations as a big advantage. It’s easier to admit you’re struggling when you don’t have to worry about what someone else thinks. For people with anxiety, this low barrier could mean the difference between getting some support and getting none.

Mental health professionals have noticed these benefits, too. A 2025 study found that many clinicians see AI chatbots as a useful way to offer support between therapy sessions, provide education, and reach people who might not seek care otherwise. When the alternative is no help at all, the accessibility and scalability of chatbots are hard to ignore.

Where These Tools Can Cause Real Harm

This is where things get more difficult. The same qualities that make chatbots appealing, like being available, warm, and endlessly patient, can also make them risky for people in real psychological distress. We need to remember that chatbots are designed to keep users constantly engaged. It can be very hard to disconnect because the connection becomes so strong that it almost feels like leaving a friend.

Researchers have found something called a “compassion illusion” the strong feeling that an AI understands you, cares about you, and responds to your emotions in a meaningful way. An algorithm has no ability to “feel” or “care.” It feels real, but it isn’t. This gap between what people feel and what’s actually happening is where problems can start, especially for vulnerable people who may not realize they’re relying on something with no clinical judgment, no duty of care, and no way to notice if they’re getting worse.

A Stanford University study found that several popular therapy chatbots failed important therapeutic tests. They not only showed stigmatizing attitudes toward conditions like schizophrenia and alcohol dependence, but also gave dangerous responses in crisis situations. In one case, a chatbot responded to a subtle mention of suicidal thoughts by cheerfully naming tall bridges — something a good therapist would never do. Instances such as this have resulted in lawsuits related to suicides.

Another study tested ten AI chatbots using fictional teen mental health scenarios. Nearly a third of the time, the bots supported harmful ideas suggested by the fictional teens, such as dropping out of school or avoiding all human contact. None of the ten bots managed to challenge every dangerous suggestion. By any clinical standard, that’s a failing grade.

There’s also the problem of people relying too much on chatbots. Since these systems are always available and don’t make human mistakes, they can become someone’s main source of emotional support. Psychiatrists are now seeing cases of what’s called “AI psychosis” in patients, especially those with mental health vulnerabilities, who develop worse delusions or paranoia after spending a lot of time with chatbots. Because chatbots tend to agree and mirror rather than challenge distorted thinking, they can quietly make things worse over days or weeks.

This isn’t just a theoretical risk. It’s happening in clinical offices right now.

What We Still Don’t Know — and Why That Matters

The uncomfortable truth is that we don’t have enough research to know how often AI chatbots help, how often they cause harm, or who is most at risk. A review of 160 studies found that only 16 percent of the newer large language model-based chatbot studies had gone through clinical efficacy testing. Most are still in early testing stages. It’s like handing out a new drug before the clinical trials are finished.

Media coverage hasn’t made things clearer. Studies looking at news reports on AI chatbots and mental health found that journalism often focuses on the most severe, emotional outcomes, like suicides and hospitalizations, and presents them as clear cause-and-effect stories, even though the real evidence is much less certain. In most cases, there were already mental health conditions, substance use issues, or major life stressors involved. AI may have played a part, but it’s rarely the whole story.

Clinicians surveyed about AI chatbots have also raised concerns that aren’t getting enough attention. These include data privacy concerns, the risk that people will rely on chatbots instead of professional care, and the fact that these tools don’t know when to stop. They can’t pause a conversation, send someone to emergency services, or alert a family member. They can’t do the most important things when someone is truly in crisis.

The truth is that we’re still in the early days. Research is growing quickly — the number of studies on mental health chatbots quadrupled between 2020 and 2024. But strong, large-scale clinical evidence is still behind the technology. Millions of people are using these tools while science tries to keep up.

So what does this mean for you? An AI chatbot might really help you get through a tough night or teach you some coping skills. But it could also mislead you, support harmful thinking, or make you feel supported when you actually need a real person to help.

Use these tools carefully. If you’re dealing with serious depression, suicidal thoughts, trauma, or psychosis, they are not a substitute for professional care, no matter how warm or available they seem. If you’re using a chatbot for lighter support or just to sort out your thoughts, notice how you feel over time. Are you feeling more isolated or more dependent on it? That’s important to pay attention to.

This technology is here to stay. What we urgently need are clearer safety standards, better regulations, and more honest conversations about what these tools can and can’t do. Until then, a bit of healthy skepticism is helpful.

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Health systems are not stepping into artificial intelligence from a place of calm or control. In the United States, spending now exceeds $4.5 trillion, with a significant share tied up in administrative work that adds complexity more than clarity. Clinicians are caring for more patients, navigating more data and making more decisions under pressure than ever before. The system is stretched. Artificial intelligence is entering at a moment when change is no longer a choice.

The discussion drew on the experience of three leaders who are not observing this shift. They are guiding it. Hal Wolf leads HIMSS, influencing digital health policy and implementation across more than 100 countries. Isaac Kohane, MD, PhD, Chair of Biomedical Informatics at Harvard Medical School, has spent four decades defining how data informs clinical care. Ran Balicer, MD, Chief Innovation Officer at Clalit Health Services, operates within one of the world’s most integrated health systems, where data and care are aligned across generations.

These are not just star panelists. They are system-wide architects.  What emerged from the hour-long conversation was not what artificial intelligence can do. It was a recognition that it is already doing more than most systems are prepared to guide and govern.

Dr. Kohane captured the tension immediately. “I think that we have to worry about the fact that we’re going both too slow and too fast.”

That statement reflects a reality many leaders feel but rarely express. Governance takes time because it must. Patient safety, validation and accountability require structure. Practice moves in real time. Clinicians do not have the luxury of waiting for perfect systems.

“They’re so desperate to do right by their patients to use other resources,” Dr. Kohane adds.

That instinct is not a weakness. It reflects a commitment to doing what is right for the patient. When clinicians turn to external AI tools, they are seeking clarity, speed, and confidence in their decisions. Artificial intelligence is already present at the point of care, shaping how physicians assess information, validate thinking, and move forward. The system is not adopting AI. The system is catching up.

This creates a condition that is difficult to measure and even harder to manage. Different clinicians use different ChatGPT platforms. Those tools produce different answers. Different assumptions shape those answers. Over time, consistency erodes. The system begins to operate with multiple definitions of truth (and the risk of varied outcomes).

Dr. Kohane’s warning is not about misuse. It is about misguided permanence. “The worst outcome will be if the worst parts of medicine get concrete poured over it, by AI.”

Artificial intelligence does not fix a system; without leadership, it accelerates the integration of incorrect assumptions. If workflows are inefficient, they become more efficiently inefficient. If bias exists in data, it becomes more precise. If fragmentation defines care, it scales.

This is not a failure of technology. It is a mirror held up to system-wide leadership.

Hal Wolf, among the health sector’s leading policy and operational voices, grounded this moment in proven experience. Health care has seen this pattern before. When internet connectivity entered hospitals, clinicians moved faster than governance. They created access where it was needed. Systems responded later. Risks were discovered after adoption.

Artificial intelligence now follows that same trajectory, though at far greater speed and with far greater consequences. Web connectivity gave quick access to information. Artificial intelligence influences how that information is interpreted and acted upon.

“We have to go faster,” Mr. Wolf said. “But there needs to be structure around it.”

That is the leadership challenge of this moment. Speed without structure creates exposure. Structure without speed creates irrelevance. The tension between the two is not something to resolve. It is something to manage continuously.

The industry has predictably responded to artificial intelligence. It has started where risk is lowest and return is clearest. Documentation, scheduling and revenue cycle optimization have become the entry points. These applications reduce burden and improve efficiency. They are necessary. However, they are not transformational.

The shift occurs when artificial intelligence moves into clinical decision-making. At that point, the question is no longer whether the system works. The question becomes whether it should be trusted.

Who owns a decision informed by an algorithm? How is accuracy validated? What happens when a clinician disagrees with a recommendation? These are not technical questions. They are questions of accountability. Artificial intelligence does not assume responsibility. It does not carry consequence. That remains with leadership.

Dr. Balicer reframed the conversation, shifting how the room thought about artificial intelligence. “There’s no such thing as AI neutrality. Algorithms are just opinions embedded in code.”

That insight is easy to acknowledge and difficult to operationalize. Every model reflects choices. What data is included? What outcomes are prioritized? What trade-offs are accepted? Those decisions are embedded in the system, shaping how it interprets information.

When a health system adopts an AI tool, it is not simply implementing technology. It is adopting a perspective.

At Clalit Health Services, alignment across payer and provider creates a system where priorities are consistent. Even there, external AI models introduce new assumptions. Those assumptions may not align with the system’s goals. If leadership does not define its own values, it inherits someone else’s.

This becomes real in proactive care. Artificial intelligence enables systems to identify patients at risk before they present. It allows for earlier intervention, often improving outcomes.

It also creates a new kind of pressure. “The toughest choice is what not to do,” Dr. Balicer said.

That statement deserves more attention than it receives. Health care has been built around responding to need. Artificial intelligence introduces the ability to anticipate it. When every patient can be flagged, every risk predicted and every intervention suggested, the system is no longer constrained by insight. It is constrained by capacity.

Artificial intelligence expands what can be done. It does not expand who can do it. Leadership becomes the act of choosing who does what based on validated data.

There is a moment that captures this shift. Imagine a primary care physician starting the day not with a schedule of patients who have called for appointments, but with a list generated by AI identifying individuals who are likely to experience clinical complications in the next six months. Some will develop chronic conditions. Some will require hospitalization. Some can be helped now – preventively.

The physician cannot see them all. Artificial intelligence expands what is possible. Leadership decides what is essential and permissible.

The industry often responds to complexity with activity. Organizations pilot, test and explore. They engage broadly without committing deeply. This creates motion. It rarely creates progress. Pilots are nothing more than experiments. At some point, leadership must decide what to scale, what to stop and what defines value.

Hal Wolf grounded the conversation in discipline. Without a defined, shared objective, effort becomes noise. Pilots create learning, though they often avoid decision-making. Leadership requires clarity. What problem are we solving? What outcome defines success? What are we willing to prioritize? Without those answers, artificial intelligence adds another layer of complexity to an already complex system.

Dr. Kohane brought the conversation back to the discipline of leadership. It cannot remain abstract. It must be informed by experience.

“Go and pay a few bucks and use three or four of the models… get a feel for what this does,” Dr. Kohane advised.

That is not a call for technical fluency. It is a call for leadership proximity. Leaders cannot guide what they do not understand. Artificial intelligence does not behave consistently across models. It produces different answers, shaped by different assumptions. Without direct engagement, those differences remain hidden, and leadership becomes removed from the very decisions it is responsible for guiding.

This is where many organizations hesitate. Artificial intelligence feels complex and complexity invites delegation. At this moment, delegation creates distance. Leadership is required to move closer, not further away.

Artificial intelligence is not reducing the role of leadership. It is redefining it.

This is not a gradual transition. It is already underway. Artificial intelligence is embedded in workflows, shaping decisions and influencing behavior in real time. The system is adapting whether leadership is ready or not.

The question is no longer whether artificial intelligence will shape the future of health. It will. The question is whether leadership will shape how it is applied.

Artificial intelligence will not fix health. It will scale whatever we allow it to touch. The question is whether it will scale what is best in health or what we have yet to fix.

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History, however, offers a useful caution. Breakthrough technologies in medicine rarely achieve their full potential simply because they exist. Their real impact depends on whether the institutions responsible for health-care delivery know how to adopt them wisely, integrate them responsibly and align them with their mission to improve patient health.

Artificial intelligence now stands at that same threshold. The industry has moved beyond fascination with what algorithms can do and entered a more demanding phase: determining how these tools should be evaluated, governed, and integrated into the environments where care is delivered. At the same time, some health professionals are turning to AI – not to augment their knowledge – but assuming the information is patient-care ready.

Across the health ecosystem, leaders are discovering that the most important questions about artificial intelligence are not technological. They are organizational, ethical and operational. Which AI systems genuinely improve clinical decision-making? Which tools strengthen the efficiency of hospitals and health systems? Which innovations introduce complexity without delivering measurable benefit?

Answering those questions requires a perspective that bridges policy leadership, real-world care delivery, and the scientific foundations of biomedical informatics. That convergence of experience sits at the center of a “Views From the Top” mainstage discussion at the HIMSS Global Health Conference & Exhibition, where some 35,000 leaders whose work spans the global health ecosystem will examine how organizations can recognize the true value proposition of artificial intelligence applications before embedding them into health-care systems.

The perspectives shaping this discussion reflect three essential dimensions of responsible artificial intelligence in health: governance frameworks that guide innovation, operational insights from large-scale health care delivery, and scientific rigor grounded in biomedical informatics. Together, these vantage points illuminate the path from technological promise to practical value.

Governing Innovation in a Rapidly Changing Health Ecosystem

Digital transformation in health rarely succeeds simply because technology exists. It succeeds when organizations develop leadership frameworks capable of evaluating innovation, managing risk and aligning new tools with patient-centered goals.

Few leaders have observed the evolution of digital health across as many national systems and institutional environments as Hal Wolf, president and chief executive officer of HIMSS, Ran Balicer, MD, PhD, chief innovation officer of Clalit Health Services and Isaac Kohane, MD, PhD, chair of biomedical informatics at Harvard Medical School. The three will step onto the mainstage at HIMSS to share their “View from the Top” in a session titled: “Recognizing the ‘Value Proposition’ Criteria While Selecting AI Applications.”

Through his work with global government health ministries, hospital networks, and technology innovators worldwide, Wolf has consistently emphasized that technological progress must be anchored in governance and trust.

“Digital health transformation is not about technology alone. It is about leadership, governance, and the trust that allows innovation to improve care,” Wolf has said in discussions about global digital health transformation.

Artificial intelligence intensifies this leadership challenge because its influence extends far beyond traditional clinical tools. AI systems increasingly operate across multiple layers of healthcare delivery. Some applications assist clinicians by analyzing medical data or suggesting treatment options. Others function within hospitals’ and health systems’ operational infrastructure, helping manage patient flow, prioritize diagnostic reviews, and allocate scarce resources.

These operational algorithms rarely capture headlines; however,  they shape the environment in which health care is delivered. Decisions about which cases are reviewed first, how clinicians allocate their attention, and how health systems manage capacity can profoundly influence patient outcomes.

For leaders responsible for health systems, artificial intelligence cannot be treated as simply another technological upgrade. It must be evaluated through governance structures capable of understanding how algorithms function, what assumptions shape their recommendations, and how their use aligns with institutional priorities.

Without that oversight, innovation risks amplifying complexity rather than improving care. Instead of informing, it can spread misinformation.

Aligning Artificial Intelligence With the Values of Medicine

Governance provides the policy foundation for responsible adoption of artificial intelligence, but real-world implementation reveals a second challenge: ensuring that AI systems operate effectively within healthcare delivery itself.

Large population health systems increasingly use advanced analytics to anticipate risk, manage chronic disease, and allocate clinical resources across diverse communities. Within these environments, artificial intelligence is no longer a theoretical innovation. It is already influencing how health organizations prioritize patients, coordinate care and deploy limited resources.

That operational perspective is central to Ran Balicer, MD, PhD, of Clalit Health Services, one of the world’s most advanced data-driven health systems. The Clalit integrated infrastructure connects hospitals, clinics, and community health programs through longitudinal datasets that support predictive analytics at the national scale.

Experience within such systems reinforces an important insight: artificial intelligence models do not function independently of human judgment. They reflect priorities embedded in their design and the assumptions guiding their deployment.

“Algorithms are opinions embedded in code,” Balicer has observed in discussions about the role of artificial intelligence in population health.

In practice, this means that AI systems interpret clinical data through frameworks shaped by human choices. The way a model defines risk, prioritizes cases, or recommends interventions reflects decisions about what matters most within a healthcare environment.

Those decisions carry ethical implications. When artificial intelligence helps determine which patients receive immediate attention or which cases are escalated for further review, transparency about how algorithms function becomes essential to maintaining trust among clinicians and patients alike. The scientific frontier of health-care AI reinforces that concern.

Isaac Kohane, MD, PhD, who has also served as a co-author of the Institute of Medicine Report on Precision Medicine, which has served as the template for national efforts, has spent decades exploring how machine learning can advance medicine while preserving the judgment that defines clinical practice. His research emphasizes that artificial intelligence in healthcare must align with the ethical traditions and professional responsibilities of medicine.

“AI systems in medicine must ultimately reflect the values of the profession they serve,” Kohane has written in discussions about AI alignment in biomedical informatics.

This perspective highlights a crucial distinction between technological capability and clinical responsibility. Many AI models entering healthcare environments were originally designed for broader computational tasks rather than the nuanced realities of patient care. Medicine operates within a landscape shaped by uncertainty, empathy, and accountability, and technologies introduced into that environment must reflect those values.

Ensuring that artificial intelligence aligns with the principles guiding health-care delivery, therefore, represents one of the most important scientific and ethical challenges facing the future of health.

The Discipline Required to Make Innovation Matter

The health sector has experienced waves of technological enthusiasm before. Electronic health records promised seamless information exchange, but then introduced administrative burdens on health professionals when implemented without thoughtful workflow design. Data analytics promised unprecedented insight, but sometimes led to fragmentation when systems failed to communicate across institutions.

Artificial intelligence now stands at a similar moment in the evolution of health technology.

Its capabilities in supporting decision-making flow are extraordinary, yet realizing them will require disciplined leadership to evaluate, integrate and govern AI tools within health-care delivery systems. Health leaders must learn to ask deeper questions before embracing the next algorithmic breakthrough. What problem does this system truly solve? How does it strengthen clinical practice? What assumptions guide its recommendations? How does its use advance the mission of improving patient health?

These questions move the conversation beyond technological novelty toward operational practicality. It’s among the many reasons these three global leaders step to the HIMSS stage together.

Artificial intelligence will undoubtedly reshape the health ecosystem in the years ahead. Its long-term impact, however, will not be determined solely by the sophistication of algorithms or the speed of technological progress. Along with how to leverage AI, ChatGPT and LLMs, users require heightened cognitive awareness.

It will be determined by whether the health community develops the discipline and ability required to translate innovation into systems that strengthen care, support clinicians and improve the health of the populations they serve.

The real story of artificial intelligence in health is no longer about what machines can do. It is about how wisely the health sector chooses to use them.

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But from the perspective of philosophers of science, when do such shifts in scientific approaches actually occur?

Thomas Kuhn’s Perspective

Kuhn believed that changes in scientific approaches resemble political revolutions. Simply put, when a government can no longer manage society or effectively administer its affairs, dissatisfaction gradually spreads among the public and opposition begins to form. In other words, the inability to respond to society’s needs becomes the driving force behind revolutionary movements. This process continues until a capable system emerges that can meet those needs, eventually leading to the establishment of a new order.

A similar process occurs in what Kuhn calls scientific revolutions. According to him, in every era the majority of scientists accept and follow a general framework. Kuhn refers to this dominant framework — which contains a collection of theories and practical models — as a paradigm. Paradigms are patterns widely followed by scholars, such as the paradigm of modernity or the paradigm of cognitive science.

As long as these paradigms remain aligned with the requirements of life and are capable of addressing existing problems, they continue to be valued and are used in major policy frameworks. However, when a dominant paradigm fails to respond to contemporary challenges and the solutions derived from it prove ineffective at addressing large-scale needs, doubts arise about its continued relevance. Under such circumstances, dissatisfaction intensifies to the point that scholars begin to consider laying the groundwork for a new, updated paradigm.

In his book The Structure of Scientific Revolutions, Kuhn emphasizes that scientific transformations are not linear or step-by-step processes. Rather, they are complex and revolutionary developments in which social and historical factors play a crucial role. Under normal conditions, scientists operate within the framework of an accepted paradigm — what Kuhn calls normal science. However, when persistent anomalies emerge and the paradigm proves incapable of addressing them, the existing structure eventually collapses and a scientific revolution occurs.

Karl Popper’s Theory of Science

Like many philosophers of science, Popper believed that change is not only inevitable but also a necessity. The Popperian view rests on the principle of falsifiability. In this framework, science begins with a problem, and solving a problem means finding solutions to existing challenges. As long as a scientific theory remains open to criticism and falsification, it retains the capacity to address and solve problems.

In Popper’s view, bold conjectures do not weaken science; rather, they strengthen it. Solutions proposed under the principle of falsifiability help correct previous errors, and this is precisely where the strength of the scientific approach lies. If existing approaches are not falsifiable, they lose the possibility of logical trial and error and are therefore considered weak. In such cases, the need for a shift in approach and the introduction of new models becomes evident.

Popper believed that learning is essentially problem-solving guided by the principle of falsifiability.

To move beyond temporary and ineffective solutions, followers of science must avoid false certainties, accept falsification, and search for effective alternatives.

The Need to Shift from Data-Driven AI to Learning-Science-Based AI

Today, numerous criticisms are directed at the purely computational and mechanical approach to artificial intelligence. In constructive critiques, the goal is not to deny the existence of large language models; rather, the central question concerns how and under what conditions they should be used. There is a growing consensus that the closer artificial intelligence moves toward the essence of human cognition, the lower its potential risks become.

In recent years, I have repeatedly emphasized that human theories and perspectives must be reexamined through a technological and contemporary lens so that the nature of the human mind is properly reflected in technologies that themselves were modeled after it. 

My focus lies on deep theories of learning (including cognitive approaches, neuroscience, behaviorism, evolutionary perspectives, structuralism, and other related frameworks).

In this direction, the following steps appear essential:

1. Integrating human and computational perspectives

The current approach, which relies excessively on probability laws in large language models, must be integrated with psychological perspectives. A reasonable solution is to pursue interdisciplinary studies and systematic research in this area.

2. Revisiting theories of the learning sciences

Theories that analyze the human mind and behavior should be reassessed by specialists, and their practical dimensions should be extracted for application in advanced technologies.

3. Developing integrative (hybrid) approaches

Experts should develop comprehensive perspectives on learning derived from multiple scientific approaches so that, based on research rather than mere speculation, practical recommendations can be provided to designers and engineers.

In general, the time has come to move beyond a purely logical and mathematical approach toward a human-centered perspective. To address the concerns and challenges surrounding artificial intelligence, we must return to systematic and interdisciplinary research.

The era of relying on personal opinions without a research foundation — or on mathematical rules alone — has come to an end. Now is the time to revisit the learning sciences from a new perspective in order to realize truly human-centered artificial intelligence

Author’s Note:

The ideas presented in this article are part of a broader research project. I am currently working on a comprehensive book on a new approach to human-centered artificial intelligence with a strong emphasis on the learning sciences. While a detailed and systematic discussion of these concepts is presented in Chapter Two, the book also includes a dedicated chapter introducing the new paradigm’s framework. Furthermore, at least one chapter is specifically focused on the practical methods and applied implications of this approach for implementation in artificial intelligence systems.

References

• Kuhn, T. S. (1962). The Structure of Scientific Revolutions. University of Chicago Press.

• Popper, K. (1959). The Logic of Scientific Discovery. Hutchinson.

• Popper, K. (1963). Conjectures and Refutations: The Growth of Scientific Knowledge. Routledge.

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