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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This is the hidden reality shaping patient care today. A life-saving treatment has little meaning if it is not within reach when a patient needs it.

Zebra Technologies’ recent hospital vision study brings this issue into sharp focus by examining materials management across health systems. The findings reveal that patient outcomes are influenced by clinical expertise and operational readiness. Nearly three-quarters of hospital leaders report that procedures or surgeries have been delayed or canceled because necessary supplies or equipment were unavailable. Each statistic reflects not an abstract system failure, but a human experience marked by anxiety, disruption, and risk.

Access the survey here.

“At Zebra, we remain closely attuned to the needs of the healthcare market. Our findings indicate that a significant majority of non-clinical leaders, 84%, consider the integration of automated, digital systems for tracking all materials used in patient care to be a key initiative for their organizations,” commented Boyede Sobitan, Global Healthcare Strategy Lead at Zebra Technologies.

In most industries, “stockouts” are a consumer inconvenience. In health, it carries far greater consequences. The absence of medication, a device, or equipment can alter the trajectory of care. It can delay treatment, extend suffering, or force clinicians into difficult compromises. The implications reach beyond logistics into the realm of patient safety, trust and survival.

This reality calls for a broader understanding of how care is delivered. Health care is often framed as a relationship between patient and clinician. That relationship remains central, yet it exists within a larger ecosystem. Supply chain leaders, inventory specialists and operational teams play a defining role in whether care can be delivered as intended. The Zebra study notes that 84 percent of decision-makers recognize that inventory management directly affects patient safety. This acknowledgment signals an important shift in thinking.

Care mat begins at the bedside, but the infrastructure that ensures continued implementation begins much earlier, in the systems that ensure readiness.

One of the more revealing insights from the study concerns something deceptively simple: language. Hospitals often lack a shared definition of what it means for an item to be out of stock. For some, it may indicate that inventory has dropped below a threshold. For others, it may mean that an item is unavailable in a specific department, even if it exists elsewhere in the system. This inconsistency creates confusion, delays response, and limits the ability to anticipate problems before they escalate.

Communication failures in health care are frequently associated with clinical interactions. This study highlights that operational communication is equally important. When teams lack a shared understanding, coordination suffers. When coordination suffers, patient care is affected.

“It’s essential that hospital teams have real-time visibility into the location and condition of their most vital resources, and we are observing a clear trend of accelerated investment in technologies that provide both location awareness and automation,” adds Zebra’s Sobitan.

The challenge is compounded by fragmentation across systems. Hospitals have invested significantly in digital tools, from electronic health records to procurement platforms. These investments have improved individual functions, yet they often operate in isolation. Data resides in separate systems. Visibility is incomplete. Teams spend valuable time searching for information or confirming availability.

This fragmentation introduces inefficiencies that extend beyond cost. It places additional strain on clinicians who must navigate uncertainty while maintaining focus on patient care. It also contributes to burnout in a workforce already managing intense demands. Time spent searching for equipment or resolving supply issues is time taken away from direct patient interaction.

There is, however, a clear path forward. The study points to a growing adoption of digital inventory management systems that bring greater standardization and visibility to supply chains. These systems create a shared source of truth, allowing teams across departments to access consistent, real-time information. When inventory levels are transparent and definitions are aligned, organizations can shift from reactive responses to proactive management.

This transition represents more than a technological upgrade. It reflects a cultural shift toward integration and collaboration. When data is accessible and consistent, decisions become more informed and more timely. When teams operate from the same understanding, coordination improves. The result is a system better equipped to support clinicians and patients alike.

It is tempting to view materials management as a background function, separate from the clinical mission. The findings suggest otherwise. Inventory is not peripheral to care delivery. It is foundational to it. Without reliable access to supplies and equipment, even the most advanced clinical capabilities cannot be fully realized.

This perspective reframes the role of operational teams within healthcare. Their work creates the conditions for care to occur. It supports clinicians by removing barriers and reducing uncertainty. It strengthens the system’s ability to deliver on its promises to patients.

At its core, this issue is about trust. Patients enter health-delivery settings with the expectation that the system is prepared for them. Providers rely on that same expectation as they make decisions and deliver care. When supplies are unavailable or systems are fragmented, that trust is tested. When readiness is consistent and reliable, trust is reinforced.

The future of health will continue to be shaped by scientific discovery and technological innovation. Those advances must be matched by equal attention to the operational readiness that enables their delivery. Investment in supply chain visibility, data integration, and operational alignment is not separate from the mission of care. It is integral to it.

Health leaders have an opportunity to rethink what constitutes innovation. It includes not only what is developed in laboratories or deployed in operating rooms, but also what ensures that those advancements reach patients without delay or disruption. Elevating materials management to a strategic priority reflects a commitment to reliability, safety, and patient-centered care.

A quiet transformation is underway in how healthcare systems approach this challenge. It is visible in efforts to standardize processes, integrate data, and connect teams across traditional boundaries. It may not generate headlines, yet its impact is profound.

The most advanced health system is defined by the skill of its patient-facing staff, the sophistication of its treatments, and its readiness to deliver care. It ensures that when a patient needs care, every piece of the operational puzzle is in place to provide it.

That readiness often goes unnoticed when it functions well. Its absence, however, is immediately felt.

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The heat on certain July afternoons was almost physical, like walking into a wall. The streets between the tall buildings formed corridors that trapped it. The asphalt radiated it upward. The buildings reflected it sideways.

There was no escape that didn’t involve an air conditioner, and every air conditioner running made it worse for everyone outside. Living in Upper Manhattan, seeing kids running in the streets where they’d opened fire hydrants for some relief from the heat was a common sight.  Even with some 18,000 trees in Central Park and another 39,000+ trees mapped throughout Manhattan, NYC is still known as a top urban hot spot.

What I didn’t appreciate then was how much worse it’s supposed to get. By 2100, cities worldwide could be as much as 4.4 degrees Celsius hotter than they are today, according to climate modeling. While I wish this were a future in some other world of the Multiverse, unfortunately, it’s a countdown already underway, playing out in hospital admissions, energy bills, and the quiet daily suffering of people who can’t afford to escape.

Here’s the uncomfortable flip side: we already know the fix, and it grows out of the ground.

The case for the urban tree

A single healthy person is a performing asset; we simply haven’t been accounting for it that way. Research by the USDA Forest Service found that urban trees in Modesto, California, returned $1.89 in measurable benefits for every $1 invested in their management, through reduced air pollution, energy savings, and increased property values.

Peer-reviewed research published in Urban Science found that urban trees sequester between 10 and 20 kg of CO₂ per year, with larger trees capturing significantly more. Nearby commercial areas have been shown to see retail activity rise by 16–18%, and properties on tree-lined streets command 3–10% higher valuations, according to McPherson et al. in Urban Forestry & Urban Greening.

When it comes to the heat island problem, a 2023 study published in Urban Forestry & Urban Greening found that full tree canopy cover reduced human heat stress by a mean 5.5°C, rising to 8.8°C during extreme heat events when air temperatures reached 40°C. Trees don’t just cool cities. They make them survivable.

That’s the reframe I want to offer: trees are not amenities. They are load-bearing components of urban systems, as functional as a stormwater pipe or an electrical conduit. When we treat them as ornamental — nice to have, first to cut in a budget — we are making an accounting error with compounding consequences.

The infrastructure failure hiding in plain sight

So why don’t more cities have more trees? Part of the answer is policy and funding. But a large part is physical. Urban streets are biologically hostile environments for trees. Research across the US, UK, and Canada documents high mortality rates — with newly planted trees especially vulnerable during the first five years. A comprehensive literature review by Hilbert et al. found annual mortality rates reaching as high as 68.5% in some newly planted cohorts. Perhaps most striking: many street trees live only 20 years or less, a fraction of their natural lifespan, because the compacted, sealed, utility-dense ground beneath city pavements starves root systems of the soil volume, oxygen, and water they need to survive.

This is the infrastructure failure hiding in plain sight. Cities are not just losing trees; they’re running a replacement treadmill, replanting in the same inhospitable conditions, spending public funds over and over for an outcome that compounds their problems. Every tree that dies young takes its ecosystem services with it: years of CO₂ capture, commercial vitality, and heat mitigation that only a mature canopy can deliver.

The investable premise

This is where the nature-as-infrastructure framing stops being philosophical and becomes financial.

If trees are infrastructure, then the systems that enable them to survive and thrive are infrastructure technology. And right now, that category is nascent, under-capitalized, and about to be turbocharged by legally binding planting mandates.

I recently spoke with the founders of TreeTube. This Israeli company has been quietly building exactly this: a patented, below-ground system that creates the conditions trees need to survive in urban environments. Made from high-density polyethylene with 25% recycled materials, TreeTube installs beneath paved surfaces and provides root systems with adequate soil volume, aeration, and water access, giving each tree its own underground life-support module. It supports heavy traffic loads above ground while protecting surrounding utilities from root damage below.

The concept sounds simple, but the execution is not. TreeTube holds patents across the US, EU, Australia, Japan, Israel, China, and more. It has been approved by utilities companies and municipalities in Israel, the Netherlands, and Estonia, and has completed dozens of projects worldwide, including alongside Tel Aviv’s light rail corridor.

What struck me most in talking with them was how naturally the product fits the infrastructure metaphor. You’re not installing a plant. You’re installing a system — modular, customizable, engineered — that happens to grow something alive inside it.

The regulatory tailwind

Investable ideas need catalysts, and this one has two.

The European Union passed Regulation 2024/1991 on nature restoration in July 2024. Starting January 1, 2031, EU member states are legally required to achieve a measurable increase in urban tree canopy cover every six years. Beyond being a goal or guideline, this is a compliance obligation with a hard timeline.

These mandates transform a discretionary purchase into a procurement requirement. Municipalities don’t need to be persuaded that trees are good; they need systems that actually work in the ground conditions they have.

The investor mismatch — and the opportunity

Here’s the honest challenge: companies like TreeTube don’t fit neatly into most VC frameworks. The returns are long-term and linked to municipal procurement cycles rather than software-style growth curves. The buyers are cities. The product solves a public goods problem. Traditional investors often see this and move on.

But impact investors should see something different. The asset here is a mandated, recurring infrastructure need, backed by regulatory law, aligned with multiple UN Sustainable Development Goals, and supported by technology with strong IP protection and real-world proof points.

What the urban tree economy needs is patient capital that understands infrastructure timelines — the kind that built water networks and electrical grids over decades, not quarters. What it offers in return is something rare in impact investing: a product that is simultaneously climate-critical, commercially validated, and legally locked in.

I left that call thinking about my Manhattan summers differently. The trees were there, just stunted and doing their best in four inches of compacted soil. Nobody had built the ground beneath them to last. That’s still true in most cities, but it doesn’t have to be.

The post Nature as Infrastructure: Why the Urban Tree Is an Investable Asset appeared first on Medika Life.

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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I believe in making it very difficult for people to refuse vaccines. There’s enough of the libertarian about me that I wouldn’t actually strap them down and inject them, but I’m fine with school districts making parents write out their conscientious objections to children being immunised or with sports clubs requiring adult proof of immunisation before people can join. What I or you think is, though, beside the point. Much of the US is walking away from cajoling and compulsion and there’s great pressure in Europe for similar change. We can either go on moaning about how we wish the world hadn’t changed or we can respond effectively.

Before the current US Administration began rewriting vaccine recommendations, one in six US parents wasn’t following them. We used to joke that vaccine-preventable diseases in the West had become diseases of children of the over-educated middle classes who shopped at Whole Foods and did naked yoga classes; vaccine refusers now are still more likely to be white, but they skew to being conservative, very religious, and young. Recommendations actually reduced uptake in this group because most have a deep distrust of the Federal Government and its agencies.

Formal vaccine refusals in Poland more than doubled from 2017 to 2022 and reached over 87,000 in 2023, a 1685% increase since 2003; measles cases surged 10x in early 2024 due to falling rates. Ireland, where I live, has the third-lowest childhood vaccine coverage rate in the OECD.

There are bright spots too, Italy for example, and the battle is far from lost. But the mistrust now endemic to the United States is coming to Europe.

High-handed US and European experts

You can understand confusion, if not mistrust. About half of parents in the USA did not vaccinate their children for flu in the past year, compared with 41 percent who said they had done so, a Washington Post / Kaiser Family Fund poll found. Coverage started declining after 2019. In 2016, the US CDC said that the nasal flu vaccine used in children provided “no measurable benefit” (injectable vaccines for adults were, as usual, highly effective). In the same year, Public Health England said that the same vaccine (produced by a British company in a British factory) was 58 percent effective. Canada followed the UK, saying that its population was very different to the USA! It’s very unlikely that both the Americans and the Canadians were right — despite those obvious population differences…. Few journalists covered the story — I suspect because no-one wanted to be accused of promoting vaccine scepticism. The vaccine is now recommended again in the USA.

Few American paediatricians and even fewer nurses would have been able to explain this to parents because no-one ever bothered to give the professionals an explanation. What do we think doctors told parents who asked why a vaccination was recommended then was not and then was again? British parents who did a web search (this was pre-Chat GPT, remember) might have asked why their children were getting an apparently ineffective vaccine and would have met equally bemused stares from their health providers. Did anyone brief social media influencers or health journalists? Of course not, who do they think they are? What impudence…

I know some of those involved and I’m sure that there was no subterfuge and nothing sinister going on; the answer is likely to be dull and involve methodology and surveillance systems.

This is the way we all used to approach treatment discussions 40 years ago — the doctor told you what to do, you thanked him (it was nearly always a him) and you did it. Questions were a sign of disrespect, of even psychological illness. I was recently treated by a Russian dentist, now practising in Ireland, who was shocked and outraged when I questioned his recommendation to use antibiotics prophylactically; if he had been Irish, he would have been completely used to it.

Nonsensical recommendations in developing countries

Vaccine hesitancy looks a bit different in France. Those least likely to have their children vaccinated tend to be more educated, high users of the internet for information and to have lower trust in health authorities. Those who refuse vaccines for themselves tend to be at the lower end of the social hierarchy with less education and fewer financial resources. Many are ​immigrants and descendants of immigrants, and residents of French overseas departments. Both are probably likely to know about the vaccines which Western experts recommend for children in the developing world, including in Francophone countries.

I remember doing a policy interview with the health minister of a large Indian state. I was trying to find out what he might pay for an effective TB vaccine. “But”, he said, “we already have a TB vaccine. Why do I need a new one?” His top civil servant was sitting behind him and frantically gesticulating to me to try to stop me explaining that the BCG vaccine, given to almost every Indian newborn, may do nothing to prevent TB infections and, at best, may make the disease less severe in some of the children who contract it. It is, though, very good at causing severe side effects. No developed economy uses it; almost every poor one does.

I’m ashamed to say that I did not explain BCG as clearly as I should have to the minister. He was the norm, not the exception, in that series of policymaker interviews: few of those making decisions about TB vaccine policy had ever been given a thorough, honest briefing about the limitations of the vaccines their expert advisers recommended. None of the parents, of course, were ever told about any of these reservations.

There might also be a case for the current practice of giving many children in Africa and Asia a vaccine that sometimes causes polio, instead of preventing it, although I doubt it. The risks of a child contracting polio from the live-attenuated oral vaccine are probably underestimated when they’re presented to politicians and policy influencers. Hardly any parents who bring their children forward for these vaccines are told about the risk or the rationale for continuing to use them, rather than the perfectly safe inactivated vaccine used throughout the rich world.

Is it any wonder that those with insight into the developing world are sceptical? The real wonder is that vaccine confidence is still so high in Africa and Asia. That probably comes from everyday encounters with the tragic consequences of infection by vaccine-preventable illnesses, an experience blessedly denied to most Americans and Europeans.

What we need to do now

The road ahead has been cleared for us. Thirty years ago, I went out with a trainee doctor at the Royal College of Surgeons in Ireland. He was upset one evening because he had been berated by his tutor for telling an older patient that she had cancer — it had been agreed with the family that she would be told that she had a “growth” to avoid upsetting her. At least she found out: King George VI of the United Kingdom sent his daughter, Princess Elizabeth, on a world tour in 1952 because neither he nor she had been told that he had lung cancer and that it was terminal. He never saw her again. These stories shock us now because honesty, realism and communication are taken for granted in what we tell patients who are ill. These principles need to be the new basis for what we tell people who are healthy and want to stay that way.

First we need a change in attitude. Whether to be immunised or not is a decision that people will take — actually, a series of decisions. We don’t need to think about whether we like the concept or not, it is the way things increasingly are. We have to get ordinary people used to making good decisions, just as they do about other life issues such as house buying or insurance or continuing education. Ordinary people are not property experts or risk analysts or trained evaluators of course offerings, but they mostly make reasonable choices. They can do the same thing with vaccines.

Then, we need to communicate much more. Vaccine producers are free to talk to the public about recommended vaccines in many countries; where they are not, they need to be allowed to. Then they need to accept their responsibility to speak often, clearly and loudly. They are the experts on the vaccines they produce and they must tell potential recipients or the parents of recipients about the benefits and disadvantages. Of course, they need to do it in an honest and balanced way. They will be more successful if they communicate in partnership with professional organisations, charities and respected consumer groups. They can be transparent: they have a commercial interest in getting people to accept vaccines but a legal responsibility to set out all the factors in deciding whether to or not. It’s like banks selling mortgages and car dealerships selling warranties.

Researchers and healthcare providers need training in communication and answering questions. They need to be much better at helping policy makers to make decisions about vaccines. Today, too few vaccines are reimbursed and many are offered only to some of those who would benefit from them. In many countries, it is still too hard to get vaccinated and even where rules have changed, practices have not — look at Poland, for example. Politicians and public officials can unleash vaccines so that they can do even more to boost productivity, growth and wealth in society.

Those same scientific and medical experts need to be much better at talking to people who are making decisions about immunisation. Research tells us clearly what helps the right decision, but too few professionals follow the evidence. The most powerful prompt to action is a trusted health professional saying, “I would like you to do this”. Setting a good example works wonders too, but too few health professionals have had all of the vaccines recommended for them.Communication can change all of this.

The vast majority of social media influencers want to give good advice and powerful motivation but no-one talks to them — after all, we want people to follow the guidelines, not think, don’t we? For example, have you seen Dr Mike Varshavski take on 20 vaccine sceptics at once? Thirty million people probably have over various platforms and he’s brilliant. Industry and professionals need to work with influencers who specialise in women’s issues, childhood, workplace effectiveness and, of course, health. Look at this from Dr Ahmed Ezzat — his videos on RSV reduced calls to the emergency services by 25% — and just think what he can do for vaccines.

Journalists are discouraged from writing pieces about vaccine decisions — “just tell people to follow expert recommendations”. Many, consequently, avoid writing about vaccines. We need to treat these journalists as powerful allies in helping lay people to make important decisions with lifelong implications for their risk of developing chronic illnesses. It’s the way that property developers treat journalists who write about houses,

Honestly, I still think it would be simpler and still ethically correct to just nudge almost everyone into getting immunised but that is not an option in many places now and, given the global market in ideas, won’t be one anywhere soon.

Parents get things right

Asia should encourage us. Many parents save and spend to get their children the best vaccines. The state often provides old tech or nothing, so middle-class parents take their children to private clinics for the best protection and pay full price for it. Of course, it’s not fair to poorer children and it is crazy public policy given that population sizes will plunge across Asia over the next 30 years so every child, whether middle class or not, is a precious national resource. Still, it shows that individual families can and do make better decisions than health policy makers when the routes of communication are open and used well.

The post We Have to Earn Better Vaccine Coverage Rates appeared first on Medika Life.

Scientific Promise and Practical Benefits

A major strength of brain organoid research is its potential to improve our understanding of neurological and psychiatric conditions. Researchers can generate organoids from people with known genetic mutations to study how specific genes affect early brain development. This method has been used to study conditions like autism spectrum disorders, epilepsy, schizophrenia, and Alzheimer’s disease. It helps scientists find cell abnormalities that might not show up in animal studies.

Brain organoids are also useful for drug discovery and safety testing. Many treatments that work in animal models do not succeed in humans, especially for brain disorders. Organoids give scientists a human-based way to test how drugs affect neural cells. This can help spot toxic effects or benefits earlier, potentially lowering the risk of expensive late-stage failures and reducing unnecessary testing on people.

Limitations, Misconceptions, and Ethical Concerns

Even though brain organoids show promise, they have important limitations that are sometimes missed in public discussions. They lack blood vessels, immune cells, and sensory input, all of which are needed for normal brain function. Because they lack a vascular system, organoids obtain oxygen and nutrients only by diffusion, which limits how large and mature they can become. Most organoids end up looking like early fetal brain tissue, not fully developed brains. Does the appearance of something mean it will have the same abilities?

Variability is another challenge. Organoids grown in different laboratories — or even within the same lab — can vary in structure and cellular composition. This makes standardization difficult and complicates the interpretation of results. Additionally, reports of electrical activity within organoids have sometimes been mischaracterized as evidence of consciousness. Most neuroscientists agree that current organoids do not possess awareness, sensation, or thought, but the debate highlights broader uncertainties about how consciousness arises in biological systems.

As the science has advanced, ethical questions have also increased. There are concerns about informed consent when donor cells are used to make neural tissue, especially if donors did not know this could happen. Other worries come up when human organoids are put into animals, which raises questions about species boundaries and oversight. Although these experiments are closely regulated, many ethicists say clearer rules are needed as the technology develops.

Future Directions and Responsible Progress

Researchers are now trying to make brain organoids more realistic and useful. They are working on adding vascular-like systems, combining different organoid types to study how brain regions interact, and making results more consistent between labs. These improvements could help us better understand complex brain disorders and lead to more personalized treatments.

At the same time, ethical guidelines are changing to keep up with new scientific advances. Many experts say that as organoid research moves forward, it should be matched by openness, oversight from different fields, and regular public involvement. Brain organoids are not miracle cures or major threats; they are powerful but imperfect tools that can help neuroscience when used carefully. The future of this research will depend on both technical progress and a strong focus on ethics and public trust.

If all of this sounds like something from a Frankenstein movie, that would be one approach to take, but it isn’t realistic. We are only at the very beginning of understanding what the potential and the problems involved are for us. The research holds great promise, but it also requires informed restrictions that will not prevent advances.

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Primary care continuity allows physicians to develop a longitudinal awareness that no episodic encounter or health tech tool can replicate. Over time, physicians learn what is normal for each patient and what represents meaningful clinical change. Subtle physiological shifts, early symptoms or emerging risk factors appear not as isolated data points from a blood exam, but as part of a social narrative unfolding across time. Early recognition allows earlier intervention, often before disease takes its profound toll.

Clinical evidence confirms the protective effect of continuity. It’s not a matter of opinion. A systematic review published in BMJ Open found that patients with sustained continuity of care had significantly lower mortality than those with fragmented care. Continuity did not just improve satisfaction; it altered survival. The physician who knows the patient can detect disease earlier and guide care more effectively.

Listening allows physicians to detect patterns that laboratory values alone cannot explain. Patients share information differently when they believe that their physician understands them and remembers their history. This sustained awareness allows physicians to identify emerging illnesses without relying solely on reactive diagnostics. Continuity transforms listening into clinical intelligence and a deeper care partnership.

In Healing the Sick Care System: Why People Matter, which has become a surprise Amazon bestseller, one insight repeatedly emerges: patients do not seek care only for treatment; they seek reassurance that someone who knows them is guiding their journey. Physicians who listen across time accumulate knowledge that cannot be captured in a chart alone. That memory allows earlier recognition, more accurate interpretation, and wiser intervention. Healing begins in that continuity of understanding.

Transactional Care Solves Symptoms but Sacrifices Understanding

Health has, for some time, been undergoing a structural shift toward transactional encounters. Walk-in clinics, urgent care centers, and virtual platforms provide speed and accessibility that patients value. These models address immediate symptoms efficiently and fill important gaps in care delivery. Accessibility has improved, yet continuity has weakened.

Transactional medicine treats episodes rather than trajectories. Each encounter begins without the benefit of longitudinal understanding. Clinical decisions are made with time-stamp specific knowledge of how symptoms emerged or how physiology has changed over time. Care becomes reactive rather than interpretive.

Research demonstrates the consequences of this fragmentation. Studies published in the Annals of Family Medicine show that sustained primary care continuity reduces hospitalizations and lowers healthcare expenditures. Early recognition prevents complications that require more invasive, costly interventions. Fragmentation delays recognition and increases clinical risk.

In fact, physicians in the vanguard of building relationships encourage their patients to ask questions.  In their co-authored book Let Patients Help! A “Patient Engagement” handbook – how doctors, nurses, patients and caregivers can partner for better care by “e-Patient Dave” deBronkart with Daniel Z. Sands, MD, MPH, the founder of the Society for Participatory Medicine, offer 10 suggestions that clinicians say to encourage patient engagement.

This shift also alters how patients engage with care. Connections that develop over time can be lost quickly when continuity disappears. Patients become consumers navigating isolated services rather than partners guided across time. The clinical relationship weakens, and with it the interpretive depth that makes prevention possible.

Health systems globally recognize the value of continuity. The Organization for Economic Co-operation and Development (OECD), a Paris-based international organization that promotes policies to improve economic and social well-being globally, reports that hospital admissions for chronic diseases, often preventable through effective primary care, account for a substantial share of healthcare utilization. Systems that preserve physician-led primary care continuity achieve better outcomes and greater efficiency. Relationship stabilizes care.

Innovation Requires Connection to Fulfill Its Potential

This shift toward transactional care carries life-threatening implications that extend far beyond the patient experience. It also directly affects whether health innovation fulfills its promise or becomes a compensatory tool addressing fragmentation. Innovation depends on context to generate meaningful insight. Context emerges through continuity. That context can devalue life-saving innovations.

Artificial intelligence, predictive analytics, and remote monitoring technologies are designed to detect patterns across time. These tools require longitudinal clinical awareness to distinguish meaningful change from statistical variation. Physicians who know their patients can interpret innovation correctly and act earlier. Innovation becomes transformative when anchored in relationship.

Fragmented care weakens this interpretive capacity. Data collected across disconnected encounters lacks coherence. Predictive tools lose precision when longitudinal context is absent. Innovation becomes reactive, identifying disease after symptoms emerge rather than predicting disease before it develops.

Technology achieves its highest value when it extends the physician’s ability to listen and observe. Remote monitoring allows earlier recognition of physiological change. Predictive analytics strengthens preventive intervention. Innovation amplifies continuity when guided by sustained physician leadership.

Team-based primary care models reflect this principle. Nurse practitioners and physician assistants expand access while physician leadership preserves interpretive continuity. Research published in Medical Care Research and Review confirms that coordinated team-based care maintains strong clinical outcomes. Physician oversight ensures that innovation remains integrated within longitudinal care. It also improves health professional job satisfaction and reduces burn-out.

Innovation cannot replace the relationship at the center of medicine. Algorithms detect patterns but do not understand meaning, and they do not strengthen physician/patient ties. Devices collect data, but do not know the patient behind the data. Physicians translate information into guidance by integrating technology with human understanding.

The future of health innovation depends on preserving continuity between patient and physician. Technology deployed within sustained relationships strengthens prevention and improves outcomes. Technology deployed within fragmented systems often compensates for structural weakness rather than transforming care. Continuity determines whether innovation fulfills its promise.

Health systems now face a defining moment. Transactional care offers speed and convenience. Relational care offers understanding and prevention. Innovation will achieve its full potential only when it strengthens the continuity that allows physicians to listen, learn, and guide patients across time.

Healing begins with being heard. Health technology succeeds when it helps physicians listen more deeply and act more wisely in the service of the people who entrust them with their care.

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Mentorship is, at its core, an educational role, and it must therefore operate on established pedagogical principles. The emergence of any new technology can reshape both concepts and practices. 

One of the most profoundly impacted areas over the last two years is “Education.” In the era of Artificial Intelligence and the race to deploy Large Language Models (LLMs), educational systems have felt the greatest impact. As global giants compete for AI investment, educational institutions are equally racing to research the qualitative and quantitative use of AI. 

Central to this is the concept of “Mentoring and Mentorship.” As the name suggests, it refers to guiding the flow of thought and performance of a human user. 

Since this process involves providing specialized knowledge to achieve a specific result, we can say a mentor is akin to a “teacher” in a formal classroom, and mentoring is fundamentally an educational concept.

Redefining Mentorship in the Age of LLMs

Both the term and the practice of mentorship have been transformed by LLMs like GPT and Gemini. Yet, despite the ease they offer, this shift is open to critique and raises significant concerns. 

Choosing an AI mentor is far more difficult than choosing a human one, because an AI is an ultra-fast intelligent machine lacking experiential history, focused instead on ultra-heavy data processing. 

Among the hundreds of apps recommended daily, three giants claim this path:

• Gemini 3 Pro: The “Analytical and Realistic” mentor. Accesses live data and all your personal files.

• ChatGPT 5.2: The “Strategic and Methodological” mentor. Provides a framework for your mental chaos.

• Claude 4.5: The “Literary and Considerate” mentor. Focused on human-like tone and output quality.

According to February 2026 statistics (LMSYS Arena & Artificial Analysis), ChatGPT 5.2 leads in reasoning intelligence, while Gemini 3 Pro excels in memory and processing speed. 

However, in mentorship, quantitative superiority is not the whole story. While Gemini is touted as analytical and exploratory, I believe further investigation is needed: 

1- Which model analyzes, and on what topics? 

2-Quantitative and mathematical? Qualitative and characteristic? In what context? 

3- Similarly, if ChatGPT is “strategic,” can logic truly be separated from data critique? Is “strategizing” not dependent on one’s unique mental background? And what, exactly, does a “considerate writer” mean in this context?

Scaffolding: Human Mentoring vs. Large Language Models

Let us compare the two. The most striking feature of a human mentor is their experiential background and their specific perception of that experience—which includes an interpretation and an emotional component. 

A human mentor provides an empirical direction shaped by cognitive and emotional dimensions alongside their knowledge. 

Conversely, an LLM is a data repository pulling from websites in real-time. It lacks lived experience and cannot integrate intuition or “gut feeling” into a decision-making system. 

While AI excels at helping with “brainstorming” by providing a vast range of references instantly, it suffers from a fundamental flaw: the absence of personal perception and the emotional weight that is vital in mentoring.

Furthermore, the stages of guidance differ. Human mentoring is a gradual, step-by-step flow. A human mentor assesses your capacity and scaffolds you accordingly. In contrast, with GPT or Gemini, there is no “scaffold.” Education is not incremental, and there is no cognitive challenge.

The model provides a massive amount of information in one or two steps. The user is pleased with the instant result, but a “missing link” remains: the user becomes perpetually dependent on the AI. They cannot independently solve subsequent challenges because they never underwent the necessary experiential and cognitive stages.

A Biological Analysis

Biologically, learning and acquisition are based on protein exchange at the neural level. This occurs when an organism encounters challenging and unknown subjects. 

According to the laws of evolution, the brain automatically triggers biochemical reactions to resolve these challenges, ultimately leading to “Learning” and “Adaptation.”

When a human mentor gradually confronts a user with their errors and potential consequences, they provide the necessary neurobiological challenge. 

This scaffolding is exactly what an evolved brain requires for “Deep Learning” to occur. However, when dealing with a “Digital Mentor,” this cognitive elasticity disappears. The process of “Cognitive Trial and Error” is compressed into a high-speed instant. 

The digital mentor dictates, and the user merely mimics and obeys. This pattern does not align with our biological necessity. Therefore, this process cannot be considered natural mentoring; it is merely “Modeling.”

Conclusion and Critical Perspective

In recent years, the surge of trend-driven discourse surrounding education and Artificial Intelligence has led to the analysis and judgment of fundamental pedagogical concepts without sufficient theoretical or empirical backing.

The oversimplification of concepts such as Mentoring, Scaffolding, and Large Language Models (LLMs) risks reducing them to mere buzzwords—widely used yet hollow. Therefore, it is essential that this movement be examined by specialists grounded in scientific evidence and core educational principles, ensuring that superficial, word-centric views are replaced by rigorous, research-based analysis.

In this article, mentoring was addressed as a dependent subset of Education—a concept that, whether in formal settings like schools and universities or in informal domains such as personal life, healthcare, industry, and business, remains rooted in the profound foundations of the learning process. Furthermore, the relationship between scaffolding, mentoring, and LLMs was scrutinized.

Based on the arguments presented, the primary challenge is not the necessity of digital mentors, but rather that these mentors are currently simulated versions, not complete replacements for human mentors. In this regard, the following questions demand serious investigation and review:

• Can development companies scientifically bridge the gaps identified in this article?

• Is it possible to integrate a form of experiential history, historical memory, and emotional/perceptual dimensions into digital mentors to truly impact a user’s deep learning process?

• Can they activate the biochemical mechanisms and cognitive friction necessary for deep learning and adaptation to new situations within the user-system interaction?

• How deep and operational is these companies’ understanding of Scaffolding, and can they genuinely integrate it into innovative design?

If a precise understanding of these gaps and challenges is formed, the digital mentors developed by tech giants could evolve beyond passive information packages. By leaning on the Sciences of Learning, they could redesign the process of educational guidance into one that is both challenging and incremental.

The core issue is not the necessity or lack thereof of the digital mentor; the issue is whether it can recreate the challenge, the experience, and the gradual process of learning, or if it will simply replace growth with speed.

References

1. Primary AI Benchmarks (2026):

•LMSYS Chatbot Arena (The industry-standard for human-preference and helpfulness ranking).

2.MMLU-Pro (The leading benchmark for advanced reasoning and multi-step logic).

3.Gemini Technical Reports 2026 (Official performance metrics for real-time data latency and multimodal accuracy).

2. Specialized Publications by the Author:

• Ferdosipour, A. (2026). Choosing an AI Mentor That Challenges Your Mind: My Statistics.

https://www.linkedin.com/pulse/choosing-ai-mentor-challenges-your-mind-my-statistics-ferdosipour-y0g2f?utm_source=share&utm_medium=member_ios&utm_campaign=share_via

• Medika Life (2025/2026). What 2025 Taught Us and What 2026 Will Demand.

• Medika Life (2026). Why Biological Learning Demands the Friction We Seek to Delete.

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The country’s trajectory has been building for years. A fast-growing middle-income population, rising chronic disease burden, and expanding health infrastructure have created both demand and urgency. What is changing now is the environment in which innovation can move, driving faster approvals, a culture of collaboration, digital infrastructure and a government signaling policy readiness to engage global partners in shaping the next era of medicine.

The economic momentum is significant. The Indian health ecosystem has expanded from roughly $372 billion in 2023 to $638 billion in 2025, making it one of the fastest-growing major health markets in the world. The broader industry is expected to exceed $610 billion by 2026, fueled by rising insurance coverage, expanding hospital infrastructure, and growing demand for chronic disease management. Health growth in India continues at approximately 10–12 percent annually, well above the growth rates typical of mature markets, reflecting both rising access and structural transformation.

BIOAsia 2026 reflects this inflection point. The global gathering in Hyderabad, themed “TechBio Unleashed: AI, Automation & the Biology Revolution,” highlights the (bio)convergence of biology, data, and intelligent systems reshaping health worldwide. Organizers emphasize that the meeting aims to drive health transformation and reinforce India’s position as a leading global life sciences force. For multinational innovators, the message is increasingly clear: India is not only where innovation is deployed; it is also where it is developed. It is where innovation is increasingly defined. India has become a go-to market for multinational enterprises.

Policy Signals and Market Scale: From Opportunity to Strategic Partnership

India’s regulatory and policy environment is evolving in ways that matter deeply to multinational innovators. One pivotal shift came with the country’s decision to allow certain medicines approved in specified developed markets to launch without local clinical trials, a move designed to accelerate patient access while aligning more closely with global regulatory science. This policy shift reflected confidence in international data, a commitment to innovation, and recognition that faster access must remain central to national health strategy.

The scale of India’s pharmaceutical and life sciences market reinforces this transformation. The pharmaceutical sector reached approximately $68 billion in 2025 and is projected to grow to more than $170 billion during the next decade, driven by expanding middle-income demand and strong domestic manufacturing. India already supplies roughly one-fifth of the world’s generic medicines. It produces the majority of global vaccines by volume, positioning the country as a central player in global health supply chains.

As Aman Gupta of SPAG/FINN wrote in Medika Life, “India’s health sector is undergoing a profound transformation, bolstered by government-led reforms and a favorable FDI regime. The allowance of 100% foreign direct investment through automatic routes in health and related sectors has already attracted global giants.” His observation reinforces a central reality for multinational innovators: India’s policy environment is increasingly designed not only to welcome global participation, but to encourage long-term strategic partnership in building the future of healthcare.

Investment trends tell the same story. Health and pharmaceutical private equity and venture investments have reached multi-billion-dollar levels annually. At the same time, India’s contract drug development and manufacturing sector is projected to exceed $22 billion within the next decade. These dynamics position India as a growth market and as a strategic partner across the innovation lifecycle from discovery and clinical development to manufacturing and global distribution.

Shakthi Nagappan, CEO of Telangana Life Sciences Foundation, captured this moment clearly, noting that BIOAsia arrives at a time when technology and biology are redefining healthcare and creating “unprecedented opportunities for innovation, investment, and impact.” The language reflects partnership rather than transaction, a signal that India is moving from market opportunity to strategic collaboration.

Digital Infrastructure, BIOAsia and the Multinational Innovation Imperative

India’s digital transformation may be its most potent catalyst for long-term health innovation. Unlike many mature systems, the country is building a national-scale digital health infrastructure designed to connect patients, providers, and health systems across a population of more than 1.4 billion people, with a rising middle class of 400 million.

The Global Digital Health Market is projected to grow from USD 288.55 billion in 2024 to USD 2,688 billion by 2035, expanding at a CAGR of 22.55% during 2025–2035. This surge is driven by the rapid adoption of AI-powered diagnostics, telemedicine, wearable devices, and data analytics solutions that are revolutionizing patient care and operational efficiency worldwide.

Hundreds of millions of citizens are already using digital health services, including telemedicine, electronic prescriptions, and remote care. The Ayushman Bharat Digital Mission is creating an interoperable national health ecosystem, enabling secure health records, improved care coordination, and population-scale data infrastructure that supports research, real-world evidence, and precision health.

For multinational companies, this digital backbone creates a uniquely strategic environment, enabling large-scale clinical research, faster pharmacovigilance, AI-supported health insights, and rapid deployment of innovation across diverse populations. India’s digital infrastructure is not simply modernizing health delivery. It is enabling national-scale transformation.

BIOAsia sits at the center of this conversation and convergence. The gathering reflects India’s ambition to lead at the intersection of biology, artificial intelligence, and scalable innovation. Leaders from industry, government, and science convene not only to discuss growth but to shape the next phase of global life sciences, where biology, data, and digital systems converge to influence global health.

One conference panel, among the many high-powered sessions, brings together global leaders in advanced therapeutics to explore how next-generation modalities are moving from discovery to scalable care. Panelists across biopharma, translational science, and hospital systems are examining progress in cell and gene therapies, mRNA, and radiopharmaceuticals, underscoring that innovation now depends as much on manufacturable scale and delivery as on scientific breakthrough. India’s expanding capabilities in clinical research and bioprocessing strengthen its role as a key partner in advancing next-generation therapies.

For multinational innovators, the implications are clear. Engagement in India now extends beyond commercialization. It calls for collaboration in research, investment in digital and scientific ecosystems, alignment with national health priorities and partnership in strengthening health delivery.

India’s Strategic Role in Global Health Innovation

India’s rise in global health innovation reflects the alignment of policy, market growth, digital infrastructure, and scientific capability forces that together are reshaping where and how healthcare innovation occurs.

For multinational companies, India now represents a full-spectrum innovation environment. It is a place to conduct clinical research across diverse populations, scale manufacturing and supply chains, deploy digital health at a national scale, and co-develop solutions addressing both local and global health challenges. Increasingly, India is not simply a recipient of innovation developed elsewhere. It is becoming a co-creator of next-generation health.

This shift changes the strategic equation. Market entry alone is no longer sufficient. Meaningful engagement requires partnership with policymakers, regulators, scientists, health providers, and digital health ecosystems. Organizations that invest in collaboration, align with national health priorities, and contribute to strengthening healthcare systems are most likely to succeed in India’s evolving landscape.

BIOAsia sets the stage for this transformation. It is more than a conference. It is a convergence of global health ambition, scientific capability, and policy momentum. The conversations taking place in Hyderabad mirror a broader reality: the geography of health innovation is expanding, and India is now central to its future.

For global health innovators, the question is no longer whether India matters. The question is how deeply they choose to engage in shaping what comes next.

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