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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These innovations promise earlier diagnosis, greater precision and improved efficiency by augmenting the knowledge and insight that health professionals develop through years of care. Yet beneath this progress lies a more difficult question. Will we use technology to strengthen the physician–patient relationship, or allow it to redefine the nature of care?

As written in Healing the Sick Care System: Why People Matter, “…the system is not broken because it lacks innovation, talent, or investment, but because it has lost sight of the people it exists to serve.” Technology is not the epicenter of care. It is meant to support communication, deepen relationships, and strengthen the human bond at the center of medicine.

Yet as artificial intelligence becomes embedded in diagnostics, decision support, documentation, reimbursement and care navigation, extraordinary clinical potential is accompanied by a growing tension.

Two Encounters, One Technology

For instance, in a primary care practice, a physician begins a routine visit with a patient in their mid-50s who has diabetes and hypertension. An ambient AI system seamlessly documents conversations, captures symptoms, updates medications, and generates a clinical note. The physician no longer turns toward a screen. Connection with the patient is essential. The patient speaks openly about fatigue, stress, and concern about long-term health.

Midway through the visit, the electronic record surfaces an AI-generated prompt suggesting an adjustment in therapy based on predictive risk modeling. The physician pauses, not to mindlessly follow the algorithm, but to ask additional questions about daily routine, financial constraints, and willingness to adopt lifestyle changes. Technology informs conversation. It does not replace it.

When the visit ends, documentation is complete, the treatment decision is shared, and the patient leaves with confidence, clarity and a sense of partnership in care. The physician directs the encounter. Technology supports judgment and understanding. The visit feels thoughtful, personal and grounded in relationship.

Now imagine the same technology in a different environment. The documentation remains seamless. The prompts still appear. The system functions efficiently. But here, the pace is set as much by operational demand as by clinical judgement. The schedule tightens. The visit is short. The physician moves quickly from one room to the next, guided less by the patient’s story and more by the system’s tempo. The encounter becomes transactional and compressed. Technology has not changed. What has changed is who is directing the care.

This is the quiet divide now shaping modern medicine. One path preserves physician-directed care, where technology supports human understanding. The other reflects system-directed transaction, where efficiency begins to overshadow the relationship. The difference lies not in the tool but in the priorities that shape its use.

This question of direction is not theoretical. It reflects a deeper shift in how technology may shape human judgment itself. Innovation theorist John Nosta, whose work has long been rooted in the health sector and now spans a broader landscape, cautions in his Psychology Today column: “Artificial intelligence is far from neutral, and we need to be careful by calling it simply a tool. By simulating understanding, it may reshape what humans expect from thinking itself. Over time, it can erode the habits required for discernment. And this danger is cumulative. It doesn’t announce itself as failure. It arrives as convenience.” Nosta is also the author of the upcoming book: The Borrowed Mind—Reclaiming Human Thought in the Age of AI.

When Technology Reflects the System Around It

Technology itself is not the challenge. When developed in partnership with physicians, nurses, and other health professionals, it can be transformative. Many of the most effective innovations emerge when developers observe the realities of care and design tools that strengthen human interaction rather than disrupt it.

John Whyte, MD, MPH, CEO of the American Medical Association, has emphasized that artificial intelligence must support physicians and care teams, not replace clinical judgment, and that technology should strengthen, not weaken, the physician–patient relationship.

A clear example of this tension is emerging in the context of prior authorization. Health professionals and administrative staff often spend more than a dozen hours each week navigating authorization requirements, time taken directly from patient care. New AI-enabled platforms, such as Optum’s Digital Authorization Complete powered by Humata Health, are designed to remove that burden by embedding real-time automation into clinical workflows and reducing manual steps. These innovations restore something invaluable: time.

Now, the deeper question is not technological but human. When time is returned to the system, how will it be allocated to the health professional? Will it allow clinicians to deepen their understanding of patient needs and strengthen their connection? Or will it simply enable the system to see more patients during their shift? The technology is neutral. Its meaning is shaped by people’s intent.

Health care operates within systems shaped by financial and operational pressures. In a transactionally driven environment, even well-intentioned technology can be redirected toward productivity rather than connection. A tool designed to restore time can become a mechanism to increase throughput. A system intended to support thoughtful care can accelerate volume in a fee-for-service environment. Technology inevitably reflects the values and objectives of the system in which it is deployed. It is not the technology that directs decisions and action; it’s the leadership.

The scale of investment underscores the stakes. The global AI in health market, estimated at roughly $36–39 billion in 2025, is projected to grow substantially in the coming decade. Investment shapes priorities. Priorities shape design. Design shapes experience. And experience shapes trust.

Emerging guidance aligned with the American Medical Association emphasizes that artificial intelligence must remain under meaningful clinical oversight. Technology must support physicians and care teams, not replace judgment or responsibility. Governance, transparency, and continuous evaluation are essential to ensure that technology strengthens patient safety, clinical reasoning, and trust.

This perspective aligns with participatory medicine. Dr. Danny Sands of the Society for Participatory Medicine has described health care not as a service transaction, but as a collaboration between patient and clinician. In that view, technology should support relationship-centered care, not redirect medicine toward system-driven throughput.

The Direction of Care

Health systems face real pressures: workforce shortages, clinician burnout, chronic disease, and financial strain. These realities demand smarter and more scalable solutions. Artificial intelligence offers meaningful progress. It can detect disease earlier, reduce administrative burden, and support more informed decisions. But efficiency is not healing.

Healing occurs when patients feel understood, supported, and guided by clinicians who have the time and space to listen and respond with care. When technology restores time and that time deepens connection, it fulfills its promise. When reclaimed time becomes additional volume, something essential is diminished.

Artificial intelligence will continue to shape medicine. The deeper question is not whether technology will advance, but who will decide how it is used and for what purpose.

If guided primarily by efficiency, care risks becoming faster but less human. If guided by partnership with physicians and patients, it can restore time to listen, space to understand, and the ability to decide together. Technology is not the healer. People are.

When guided by clarity of purpose, with the patient at the center of effort, and grounded in physician-guided judgment, technology becomes what it was always meant to be: a force that strengthens knowledge, deepens understanding, and restores the bond between physician and patient. Systems matter. They enable scale, coordination, and progress. Yet their purpose is fulfilled only when they serve people. Health care is at its best when human connection and well-designed systems work together in the service of healing.

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Thought with purpose.

The Human Side: Purpose as the Compass

For us humans, thought doesn’t just tumble out of nowhere. Even a simple thought is tethered to something such as a memory, a need, or even a curiosity. The purpose is always there, sometimes in plain view, sometimes we barely notice it’s steering us. Nevertheless, it’s there.

That orientation towards an end, whether it’s solving a problem, telling a story, or making sense of loss, shapes everything. It sharpens context, gives weight to our choices, and carries consequences forward.

The Machine Side: Output Without an Inner Why

Now, here’s the curious part, large language models can produce work that looks like it was driven by intent. But the intent isn’t theirs. The “why” behind the output is always imported from a prompt, a training objective, or a line of code.

Even Yoda, the unlikely techno-philosopher of a galaxy far, far away, hinted at this kind of thinking. His counsel to Luke was often binary: “Do. Or do not. There is no try.” In moments like this, the Jedi master stripped away contemplation of purpose in favor of pure execution. It’s a kind of “ateleological” mindset, where output emerges without interrogating the why.  And that has its place in discipline and training. But for us, this is the exception, not the norm. Our thinking almost always is driven by a goal, even when we’re not consciously naming it.

LLMs begin with patterns, not with goals. They finish with polished coherent text, but without ever having set out to “do” anything. This is the inversion I’ve called anti-intelligence—completion without intention, or perhaps better said, performance without the inner compass that orients human thought.

Yes, the Lines Blur

It’s easy to miss the difference. A well-crafted AI essay can read like the work of someone with a clear aim. That’s because we humans are wired to project purpose onto anything that speaks coherently. It’s how we’ve always communicated and to assume a mind with goals is on the other side of the words.

But mistaking thought without purpose for thought with purpose isn’t harmless. It can shift decisions into the hands of systems that can’t weigh values, and make scale look like judgment. And perhaps most insidious,  it can dull our instinct to ask why something was said in the first place.

The Partnership That Works

This doesn’t make AI lesser. In fact, the difference is what makes it valuable. Humans bring the “why.” AI brings the “how” and it can deliver that “how” at a speed and scale we’ll never match.

The essential challenge is keeping the two in their proper lanes, even when a curious cognitive emulsion sometimes emerges. When human purpose sets the direction and AI handles the reach, the result is something neither could accomplish alone. Lose that clarity, and we start letting pattern-generation masquerade as goal-driven thought.

Now, More Than Ever

More and more, the content filling our feeds, inboxes, and dare I say, heads, will come from systems that simulate purpose without ever possessing it. Forget that distinction, and we risk letting the “performance of intelligence” replace the reality of it. That’s a shift we can’t afford.

Thought with purpose is more than a phrase. It’s a reminder that the thinking worth trusting comes from goals we choose, meaning we make, and consequences we’re willing to own. It is the perfectly imperfect part of being human that no machine will ever replace.

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A Conversation Without Borders

Among the diverse voices at the Forum were three standout attendees—one from Ghana, another from Brazil, and still another from India—all deeply committed to advancing scientific discovery and digital transformation in health, all sitting at one table coincidentally. Their presence reinforced the idea that innovation emerges not from a single system or nation but from a mosaic of lived experiences, cultural insight, and shared human purpose.

At a time when geopolitical divisions grow and xenophobic rhetoric clouds practical need, this convening of minds from across continents stands as a counterpoint: progress in medicine and public health demands openness, not isolation.

Today, two out of five HIMSS members live outside the United States, representing the tremendous growth in its international reach.

Global Minds and Shared Missions

Consider the stories behind some of the most transformative scientific breakthroughs. Dr. Tu Youyou, who drew upon traditional Chinese medicine to isolate artemisinin, reshaped malaria treatment and saved millions. Tu received the 2011 Lasker Award in clinical medicine and the 2015 Nobel Prize in Physiology or Medicine jointly with William C. Campbell and Satoshi Ōmura for her work.

Dr. Salvador Moncada, born in Honduras and later based in the UK, changed the future of cardiovascular medicine through his work on nitric oxide. And Dr. Pardis Sabeti, born in Iran and raised in the United States, played a critical role in genomic tracking during the West African Ebola outbreak. These are not anomalies—they are the natural result of cross-border learning and purpose-driven science. In recognition of his tapping into the power of collaboration to accelerate biomedical discoveries, Dr. Salvador was nominated by the President of Honduras to serve as the country’s first Ambassador to China. 

Such examples underscore a larger point: global health challenges—from infectious disease to chronic illness—cannot be solved in silos. They require knowledge sharing, inclusive research, and the integration of clinical science, population health data, and epidemiological insights gathered across geographies. HIMSS is paving the way for people and countries to come together.

Today, health information flows freely across continents. Clinical trials are increasingly multinational. Genomic datasets used to train AI models include samples from diverse populations. Epidemiological patterns—from outbreaks to noncommunicable disease trends—are informed by data from regions that span income levels and infrastructure capacity. This global interconnectedness of knowledge is not only valuable—it is vital.

Health innovation now depends as much on access to ideas and information as on access to raw data or funding. Each individual—whether a clinician, data scientist, policymaker, patient or communicator—contributes to this ecosystem through their choices within their workplace, organization, advocacy group and community. These local actions ripple outward to impact global outcomes.

When people are empowered to think boldly and act collaboratively—regardless of where they are from—their influence transcends borders. This is especially true in a world where diseases migrate, health inequities persist, and environmental factors increasingly shape population health. No one country has a monopoly on the future of medicine, and no one person is immune to illness.

Science and Technology as a Bridge

Science is not merely technical; it is relational. It is built on trust, transparency, and the willingness to share. When data is exchanged openly—on disease trends, therapeutic outcomes, or environmental health risks—it becomes a force for public good. When it is withheld or politicized, it delays solutions and costs lives.

As HIMSS convened global thinkers in a borough symbolic of reinvention, the message was clear: advancing AI in health is not just about algorithms—it’s about equity, empathy, and inclusion. Those values begin not with policy mandates but with people. Beneath sessions on technology and policies, the conversation continually returned to the reality—it’s about people working collaboratively.

Every organization has the power to foster a culture where global voices are welcomed, collaboration is incentivized, and ideas are judged not by origin but by merit. The future of health will be shaped by how willing we are to embrace human potential, wherever it begins, and work with people who can help advance human health wherever they call home.

Brooklyn as a Setting and Symbol

Brooklyn is a fitting backdrop for these conversations. A city defined by generations of immigrants—scientists, healers and visionaries—stands as a beacon for what is possible when people are welcomed, not walled off. HIMSS is hosting its APAC meeting July 16-18 in Malaysia.

The HIMSS AI in Healthcare Forum brought together technologists, clinicians, ethicists and entrepreneurs. But more than that, it reminds participants of something timeless: when diverse minds come together, knowledge is not only shared—it is elevated. When human potential is honored without prejudice, the possibilities for better health are limitless.

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AI Is Not an Add-On—It Is the Infrastructure

One of the most powerful refrains in Lawry’s address was this: Artificial Intelligence (author’s definition – “augmented implementation”) is a general-purpose technology. Like electricity or the printing press, it doesn’t simply optimize existing processes—it refines them. It changes the sector’s (and perhaps society’s) operating system. It is not “intelligent,” it’s intelligence, [as John Nosta suggests].

In the health sector, AI must be treated not as a pilot project or a staffing replacement but as core infrastructure. It’s not a department, it’s a foundation. Lawry urged leaders to go beyond adoption cycles and recognize AI’s capacity to reshape systems, relationships and responsibilities. That shift requires not just technical integration but cultural transformation. It requires integration of clinical, operational and human resource functions.

“What Does This Mean for Me?”

Clinicians aren’t resisting AI—they’re seeking relevance. Their reflective question is: “What does this mean for me?” This question is surfacing across hospitals, clinics, and systems worldwide. Health professionals are not asking for more white papers or coding walkthroughs—they want to know if their judgment, autonomy, and voice will be protected.

His message was clear: Don’t ask clinicians to adopt. Invite them to co-design. Empower them to lead alongside technologists. That is how AI earns trust and ensures value.

Elevating the Workforce Through Upskilling

Referring to McKinsey forecasts, Lawry noted that up to one-third of clinical activity—primarily administrative—can be automated. But this is not about eliminating jobs. It’s about restoring the joy of practice and aligning people with purpose. If deployed wisely, AI can liberate talent from tasks that dull passion and delay patient care. The real challenge will be forging a bridge between aspirational and operational intent.

This is possible if health systems democratize AI knowledge. Upskilling cannot remain the domain of senior executives and IT teams. The professionals most affected by AI must also be those most prepared to use and question it with confidence.

Governance Is the Bedrock of Responsibility

Too many institutions speak about responsible AI but fail to structure it. As Lawry outlined and reflected in his Responsible AI Discussion Guide shared at the Forum, governance is not an aspiration. It is a requirement.

Lawry asks three key questions to test organizational readiness:

1. Has the institution formally adopted responsible AI principles, ratified by top leadership or the board?
2. Are those principles consistently applied to all AI projects and partnerships?
3. Are AI standards written into procurement practices?
   
   The health sector cannot afford partial answers. Most AI will arrive embedded within larger platforms—EHRs, diagnostics or billing systems. Governance alone is insufficient if the umbrella of its use doesn’t extend to vendors and embedded solutions. His cautionary guidance: policies without enforcement expose organizations to reputational and regulatory risk.

Judge AI by Outcomes, Not Headlines

It’s easy to get excited about the tech tools and early pilots. However, Lawry warned against evaluating success by the number of AI deployments. Actual value must be measured by mission alignment. That means, are outcomes improving? Are clinicians regaining time and focus? Are costs becoming more sustainable? Is ethical compliance being elevated? It is a nuts-and-bolts call-to-action.

Lawry urges organizations to treat AI not as a buzzword, but as a continuous improvement program. Like clinical quality, it requires constant evaluation and a clear connection to purpose.

Leadership Is the Deciding Factor

Perhaps the most lasting message from his keynote is that technology does not create transformation—leadership does. From boards to bedside, AI requires a mindset of clarity, courage, and consistency.

Leadership must:
– Understand what AI can—and cannot—do
– Create a culture where experimentation and transparency thrive
– Build governance, avoiding treating AI as a vendor offering

“AI value at scale is about leadership,” Lawry declared. “No algorithm, no matter how powerful, can substitute for moral clarity and institutional courage.”

A Gathering That Signals Momentum

This HIMSS AI in Healthcare Forum is a chance to compare notes and is a change-agent catalyst. It brought together visionaries and practitioners, policymakers and informaticists, engineers and ethicists. What unites them is the understanding that we are not waiting for the future—we are in it.

From HIMSS CEO Hal Wolf’s empowering opening remarks to Forum Master of Ceremonies and HIMSS Senior Director of the Personal Connected Health Alliance Rob Havasy and the powerful opening keynote by Tom Lawry, it is clear that this event is more than a “professional meeting” – it is an invitation to the possibilities of the age of AI in the health sector. But its success will not come from software, coding and flashing healthtech alone. It will arise from systems that put people at the center—patients, providers and the health ecosystem community.

AI Has No MRI: It’s All About Leadership

Tom Lawry offered more than a presentation—he provided a roadmap. One that begins not with technology but with trust. One that demands more than innovation—it requires intention.

The transformation ahead is not only technical. It is cultural, operational and profoundly human. The institutions that rise to the occasion will do much more than survive disruption. They will define the next era of healing.

AI is here. The question now is whether we are ready to use it and lead with it.

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That changed something, even if we didn’t notice it at first.

There was a kind of wonder in seeing language freed from memory and effort, from time and constraint. We wanted to see what knowledge looked like when it didn’t have to be learned. When it could simply be summoned. So, we opened the interface, glowing and ready. And what we found was smooth and seductive. Answers arrived without hesitation, just coherence on cue. And for a moment, we believed, including me, that maybe this was the future. Not just of information, but of thought itself. And I even called it The Cognitive Age.

But something else entered the room. It was a quiet shift in how we think, in what we trust, in what we now take as presence. It didn’t just offer a tool, it offered a new architecture for cognition. Slowly, almost imperceptibly, we began to tune ourselves to its rhythm. We adapted to something that simulates intelligence without ever understanding. What I’ve come to call anti-intelligence. A coherence engine that looks like thinking but isn’t.

Still, it’s useful. Students rely on it to learn. Writers use it to craft their narratives. Therapists use it to summarize long, tangled stories. Certainly, it makes things easier. But perfection doesn’t stay still. Once introduced, it often begins to steer and even drive. At first, we admired the fluency. And then, without much varied fanfare, we let it set the pace.

What we’ve lost is easy to miss. We used to find meaning in the struggle. In the clunky sentence, the pause and even in the contradiction that didn’t resolve. These weren’t flaws, they were signs of someone thinking. But machine logic doesn’t like friction. It uses the hammer of statistics to smooth and brings things to a cohesive conclusion. And somewhere in that shift, the simulation began to feel more real than the flawed voice it was supposed to support.

This isn’t just about tone or writing style, it’s about how we shape thought. AI doesn’t think, but it “performs thinking” so well that we start to believe it does. And when that performance becomes our standard, we adjust ourselves to it.

Curiously, effort may start to feel inefficient. If the answer arrives polished and complete, why struggle? But the struggle is the very thing that gives thought its shape. It’s not noise, it’s the signal. It means someone is reaching and working to understand. Too often, the effort is faked. The surface looks right. But nothing was ever carried to get there.

And the more we grow used to the polish, the less we tolerate the real work behind it. We lose patience with what once made us human. Those defining moments of imperfect moments of doubt, curiosity, and hesitation. That’s what’s being eroded, not just facts, but the expectation that meaning takes time. That truth, when it shows up, carries with it some resistance.

The simple truth is that ambiguity used to be a space we entered, not a flaw we tried to fix. We still hold on to the Mona Lisa for a reason. Not because her expression is clear, but because it isn’t. Her face doesn’t resolve, but it lingers in a poetic injustice to finality. And that used to mean something. But systems built to optimize don’t linger. They conclude and finalize. Push a button and they collapse possibility into answer. And as we spend more time with them, we begin to mirror them.

In the myth, when Pandora opened the box, everything was released but one thing. And I think that it was hope stayed behind. And maybe it still does. Maybe it lives in the rough sentence we haven’t fixed. The thought we haven’t quite found the words for. The moment we choose to write on our own and let perfection be damned. Or maybe, and perhaps most importantly,  hope my lie in what AI can’t do.

Because if we forget how to reach, how to wait, how to not know, then we lose more than just voice. We lose the raw material of thought. And somewhere in the unfinished space, in the gap between what we mean and how we try to say it, something honest and something very human still survives.

Something the machine has not yet learned to fake.

Us.

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He is a second-generation physician who prefers to be called by his first name, honoring his father, “the original Dr. Grossmann.”  In his own right, he’s a trailblazer at the nexus of surgical care and innovation. Born in Caracas, Venezuela and carrying forward his family’s medical legacy, he completed his surgical residency in Ann Arbor, Michigan, before establishing his practice in New England, serving as a general, trauma, advanced laparoscopic, and robotic surgeon at Portsmouth Regional Hospital in New Hampshire and Eastern Maine Medical Center.

Rafael is frequently linked to his groundbreaking use of Google Glass during surgery. But to define him by that singular innovation is to miss the deeper force driving his work: an unwavering belief that technology must serve—not supplant—the doctor–patient relationship. In recent interviews and longstanding contributions across digital health platforms, Rafael shares an increasingly urgent message: humility and empathy are not soft skills of the past—they are foundational elements of the future.

Proximity Over Performance
Rafael’s approach to technology is both deliberate and human-centered. He integrates AI, extended reality, and telehealth into care environments with one goal: to foster proximity between healer and patient. Whether bringing loved ones into ICU rooms through virtual tools, using augmented reality to teach medical trainees, or deploying wearables to enhance surgical insight, his purpose is consistent: technology must deepen the human connection.

“If the technology doesn’t enhance the connection between physician and patient,” Dr. Grossmann notes, “it has no role in care.”

That conviction reflects a broader truth in modern medicine: innovation must be guided by intention. The impact of a new tool is not measured by its complexity, but by its capacity to sharpen listening, expand compassion, and build trust. In this view, humility is not an abstract virtue—it is a clinical competency.

Humility as a Clinical Skill
While empathy is increasingly recognized as a measurable component of quality care, humility remains underappreciated. Yet humility—the ability to acknowledge limits, listen fully, and elevate the patient’s needs—may be one of the most critical skills a clinician can develop.

Rafael challenges medical education to do more than train for outcomes; he calls for cultivating presence. In trauma settings and academic halls alike, he models humility not as passivity, but as active, intentional leadership. It takes courage, he says, to be honest with patients—not just about diagnoses, but about uncertainty.

“The best medicine,” he reflects, “comes from presence, not only performance.” In high-tech environments where algorithms analyze and recommend, the clinician’s humility may be the most human—and healing—intervention available.

Empathy, Elevated by Innovation
To Rafael, empathy and innovation are not opposites. When used wisely, technology can extend—not replace—the clinician’s presence. Telemedicine platforms become conduits for comfort. Immersive simulations train for compassion. Data becomes dialogue when interpreted with care.

This mindset is especially important now. Patients today may have unprecedented access to information, yet they often feel unseen. In an age of instant answers, the experience of being truly heard remains rare. Rafael reminds health-sector leaders and policymakers that no system—however advanced—can succeed if it forgets the people it was designed to serve.

Clinicians stand at a crossroads as health delivery accelerates toward predictive analytics and AI-driven decisions. Technology offers an undeniable opportunity: greater access, improved accuracy, and better outcomes. But these advances must be matched by a return to the timeless principles of great medicine—empathy, humility, and presence.

Rafael’s work represents a rare blend of innovation and introspection. His willingness to explore the boundaries of digital medicine is matched by a steadfast insistence that patients remain at the center. The future of care, he contends, won’t be defined by who uses the most sophisticated technology, but by who uses it to deepen human connection.

Rafael is not focused on being remembered for the tools he introduced. He hopes to be known for something quieter: helping patients and clinicians feel seen, heard, and supported.

In an era when health systems are rethinking priorities, medical schools are reassessing competencies, and companies are racing to redefine care delivery, the voices of clinicians like Rafael’s matter more than ever. Humility, after all, is not the opposite of expertise—it is its most authentic expression.

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That moment, when the T-800, a machine built for destruction, understands human emotion, is among the most powerful in action cinema. It is the climax of Terminator 2: Judgment Day, but also a beginning: the start of the android’s transformation, not into a human, but into something more self-conscious that recognizes the worth of organic life, even if it can outthink people, it can appreciate the human experience.

The metaphor feels timely as we stand at the edge of an AI-driven health future. Today’s GenAI tools are evolving rapidly, but are we, their creators and coders, evolving with equal intentionality? Are we teaching the owners of these systems why we heal, or just how?

We often speak of artificial intelligence as if it were separate from us. But AI is not alien. It is us—our ideas, data, values—encoded and amplified. It mirrors back what we feed it. In the realm of health, that reflection must be carefully considered. Unlike a Hollywood villain, GenAI doesn’t turn against us with malicious intent. But it can misalign from its purpose if we forget that behind every innovation must be a human-centered goal.

From the first recorded prayer for healing in the Bible—“G-d, please heal her now”—health has always been rooted in empathy, intuition, and relationships. The clinician’s pause before giving a diagnosis, the nurse’s touch when comforting a patient, and the community health worker navigating skepticism in underserved areas are not functions you can replicate with an algorithm. They are acts of presence, of judgment shaped by experience and emotion. Yet, technology now surrounds these moments, offering powerful new support.

Even Satya Nadella, CEO of Microsoft, captured this imperative clearly: “Empathy must be embedded in artificial intelligence from the moment it is created to ensure it becomes a positive force in people’s lives.” It’s not just about what technology can do—it’s about how it’s directed, and who it serves.

GenAI is already beginning to assist clinical teams by synthesizing medical records, supporting drug discovery, and interpreting diagnostic images faster than human eyes. It scales knowledge, translates complex science for patients, and identifies early signals of population health risks. These are welcome advancements—but only when guided by a human compass.

Let’s not look at a future of “us vs. them”—patients and providers versus machines. The more accurate framing is “us and them”: a coalition of human and machine intelligence, working together in the service of healing. Patients, payers, providers, product developers, and policymakers are the “us.” GenAI, LLMs, machine learning, and chatbots form the “them.” Power lies not in one side dominating the other, but in how we integrate these efforts.

Tim Cook, CEO of Apple, has often said, “At Apple, we believe technology should lift humanity.” In a world driven by rapid innovation, his words are a steady reminder that progress without purpose is not progress—it’s motion without meaning. Cook also noted at MIT, “Technology is capable of doing great things, but it doesn’t want to do great things. It doesn’t want anything … That part takes all of us.”

To do that, we must resist the urge to see AI as an all-knowing oracle. AI is not autonomous in values, does not possess a conscience, and lacks intuition unless we teach it patterns. Those patterns, if drawn from biased data, can replicate systemic inequities. In health, where trust is everything, we cannot afford such blind spots. Human oversight is not just necessary, it’s irreplaceable.

There’s also a danger in assuming technology alone can fix what’s broken. We already know the limits of scale without empathy. We’ve seen systems become more efficient but less personal. We’ve witnessed patients lost in data flows, their lived experience reduced to metrics. If GenAI becomes another layer of distance rather than connection, we will have failed to grasp its most powerful potential: to bring clarity, not complexity; to extend human capacity, not replace it.

OpenAI CEO Sam Altman acknowledges the promise and the peril: “This will be the greatest technology humanity has yet developed… We’ve got to be careful here … people should be happy that we are a little bit scared of this.” Fear, in this case, signals responsibility. Responsibility requires centering AI in the service of people, not pushing people to conform to the logic of machines.

There are lessons in Terminator beyond the thrill of a dystopian chase. Sarah Connor learns to trust the very machine that once tried to kill her. John Connor, the future leader of humanity, becomes the teacher. And the T-800—a symbol of cold efficiency—becomes the student. This reversal reflects what we need now: machines that learn how to act and why their actions matter, not just how to optimize workflows but why saving time matters when time is the difference between life and death.

We cannot forget how this transformation from killer machine to protector occurs. In “Terminator 2: Judgment Day,” the T-800 model evolves into humanity’s hero because John Connor reprograms it from the future to protect his younger self and his mother, Sarah Connor. The human is the creator—the coder.

Somewhere in this cinematic science fiction lies a guiding truth for our future reality: technology learns from humanity. Just as this version of the Terminator changed by being close to people, our AI systems will evolve based on what—and who—they are near. If surrounded by empathy, equity, and ethical standards, they can amplify what’s best in us. If left untethered from human purpose, they risk scaling our worst habits.

We often frame digital health progress in terms of speed and scale. But what if we reframed it through the lens of dignity? What if the measure of innovation wasn’t just how fast a model can generate results, but how well it supports the human healing experience?

In the end, the T-800 sacrifices itself to protect a better future. It understands that some decisions aren’t logical; they are meaningful. It doesn’t cry—but it finally sees why we do.

Let’s not wait for machines to catch up with our humanity. Let’s lead with it.

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Artificial Intelligence (AI) is rapidly reshaping public health — from enhancing disease surveillance and diagnostics to easing workforce burdens — but it also raises complex risks and ethical questions. In Europe and globally, public health leaders are grappling with how best to harness AI’s revolutionary potential while managing its pitfalls. After decades of experience, many recognise that AI is not a magic fix for health challenges; its value depends on thoughtful integration into health systems. This article provides an in-depth review of the current relationship between AI and public health. It examines the opportunities it offers, real-world innovations already underway, practical implementation challenges, and the risks and governance frameworks that must guide responsible use. All discussions equally consider European contexts (including emerging EU regulations) and broader global health perspectives.

TL;DR Summary

• AI’s growing role in health: Artificial intelligence is increasingly used to augment public health efforts — from automating administrative tasks to advanced disease surveillance and diagnostics — offering new ways to improve efficiency and reach.
• Tangible benefits observed: Early deployments show promising results. AI tools have reduced clinicians’ paperwork burden, flagged outbreaks days before traditional systems, and enhanced diagnosis in low-resource settings (e.g. catching 15% more TB cases via X-ray analysis).
• Innovations across sectors: NGOs, governments, and companies are all investing in AI for health. For example, PATH and others use AI in field programmes, the NHS has dozens of AI pilots improving care delivery, and pharma companies leverage AI to speed up drug and vaccine development.
• Practical hurdles remain: Successful implementation requires robust data infrastructure, interoperability, and high-quality data. Many health systems must modernise IT systems and address data silos and quality issues before AI can perform optimally.
• Human factors are critical: Integrating AI into workflows and gaining staff acceptance are significant challenges. Training health workers, providing explainable outputs, and maintaining human oversight are essential to building trust in AI-assisted care.
• Key risks to manage: AI in public health brings serious risks — privacy breaches, algorithmic bias harming disadvantaged groups, opaque “black box” decisions undermining trust, and AI-generated misinformation spreading false health advice. Over-reliance on AI without safeguards can also be dangerous.
• Ethics and governance frameworks: Clear principles and regulations are emerging to guide responsible AI use. WHO’s six ethical principles (e.g. transparency, equity, accountability) set value-based guardrails, while the EU’s AI Act will enforce strict requirements on high-risk health AI (mandating transparency, risk management, and human oversight).
• Collaboration and capacity-building: Effectively advancing AI in public health will require interdisciplinary collaboration (health experts with technologists), investment in workforce AI literacy, and inclusive approaches that involve LMICs and marginalised groups so benefits are shared widely.
• Continuous evaluation and adaptation: To ensure AI delivers on its promise, public health authorities must continually monitor outcomes, audit algorithms for bias or errors, and be ready to adjust or suspend systems if problems arise. Adaptive governance and ongoing community feedback are vital for safe, effective AI integration.
• Seizing the opportunity responsibly: When guided by ethical principles and strong oversight, AI can greatly strengthen public health, easing workforce burdens, expanding outreach, and providing data-driven insights. The next few years are crucial for implementing the policies, education, and trust-building measures that will allow AI to be a force for health equity and innovation rather than a source of new disparities or dangers.

Opportunities: Transforming Public Health with AI

AI is being deployed to alleviate several longstanding public health challenges. One significant opportunity is reducing clinician burnout and workforce shortages by automating routine tasks. For example, a 2024 survey found that 57% of physicians believe automating administrative burdens is the top opportunity for AI to ease workloads amid staff shortages. Machine learning systems can transcribe medical notes, pull up patient records, and handle scheduling or prescription refills — freeing clinicians to spend more time on patient care. Many doctors see such automation as a key to improving work efficiency and reducing stress, suggesting AI could help mitigate the healthcare burnout epidemic.

AI also offers powerful tools for disease surveillance and epidemic intelligence. Algorithms can continuously scan vast data sources — news reports, social media, travel data — to spot early signs of outbreaks far faster than traditional methods. Notably, the HealthMap and BlueDot platforms (which use natural language processing and machine learning) flagged the COVID-19 outbreak days before official alerts. By sifting through informal signals and anomalies, AI-driven systems can provide precious early warnings of emerging health threats. BlueDot’s AI surveillance tools have dramatically sped up outbreak detection, reducing manual scanning time by nearly 90% in some cases. Such early alerts enable public health agencies to mobilise quicker responses and potentially contain outbreaks before they spread.

Another area of opportunity is improving diagnostics and clinical decision support, especially in resource-constrained settings. AI image recognition has shown great promise in interpreting medical images like X-rays and retinal scans. For example, AI-based chest X-ray tools for tuberculosis (TB) are being used to help screen patients in low-resource areas that lack radiologists. A recent programme in India led by PATH found that an AI tool (qXR) boosted TB case detection by ~15.8% — identifying cases that human readers missed. Many countries are now utilising AI-assisted chest X-ray screening for TB, which can lead to earlier diagnosis and treatment in underserved communities. Beyond imaging, AI-powered diagnostic apps and chatbots can guide patients through symptom checks or flag high-risk cases for follow-up, expanding access to essential healthcare advice where clinicians are scarce.

Crucially, AI is also being enlisted to address climate-related health threats and environmental impacts on health. Public health researchers increasingly pair AI with climate data to predict disease patterns under changing environmental conditions. For instance, machine learning models can correlate weather patterns (temperature, rainfall) and even animal health data with disease outbreaks to anticipate risks in specific locations. By analysing such data, AI-driven predictive analytics can serve as early warning systems — forecasting surges in vector-borne diseases like malaria following heavy rains or heat-related illness during extreme heatwaves. This capability is ever more critical as climate change intensifies health hazards. AI can help public health officials prepare for climate-sensitive disease outbreaks, allocate resources proactively, and develop adaptation strategies to protect vulnerable populations.

Real-world Applications and Innovations

AI in public health is not just theoretical — numerous real-world initiatives by NGOs, governments, and private companies have already demonstrated its potential. Global health nonprofits and international agencies have been early adopters of AI to support their missions. For example, the Bill & Melinda Gates Foundation has invested heavily in AI-driven global health projects. In 2023, it awarded grants to nearly 50 pilot projects exploring AI solutions for health and development challenges — these range from AI-augmented diagnostic tools to data systems for disease surveillance in low-income settings.

One Gates-backed innovation is AI-assisted ultrasound: in 2020, a $44 million grant was given to develop an AI-guided portable ultrasound to improve lung disease diagnosis in low-resource countries (e.g. detecting pneumonia). Likewise, PATH and other NGOs are integrating AI into field programmes — as seen in the TB screening project, where an AI tool significantly increased case finding while illuminating practical deployment hurdles. These efforts by NGOs underscore AI’s promise to close gaps in healthcare access and quality for underserved populations.

Governments and public health agencies are also launching AI initiatives. In Europe, national health systems pilot AI to improve services and efficiency. For instance, the UK’s National Health Service (NHS) created an NHS AI Lab to fund and evaluate AI innovations in care delivery. By 2025, the NHS had over 80 AI projects live, targeting everything from optimising nurse rostering and predicting hospital bed occupancy to speeding up radiology workflows.

One NHS program provided £100+ million in awards to develop AI for earlier cancer detection, resource management, and patient safety improvements. The NHS AI Lab’s “Skunkworks” team has run short-term projects that yielded practical tools — e.g. an algorithm to streamline the placement of nurses across wards and a natural language processing engine to search health records more efficiently. Meanwhile, European public health agencies are leveraging AI for epidemiology; the European Centre for Disease Prevention and Control (ECDC) has incorporated systems like BlueDot’s AI to enhance epidemic intelligence, including monitoring outbreaks during events such as the 2020 Olympics. These government-led efforts illustrate growing public sector commitment to deploying AI for health system strengthening and emergency preparedness.

The private sector, particularly in healthcare and pharmaceuticals, is likewise driving innovation at the intersection of AI and public health. Pharmaceutical companies now routinely use AI in drug discovery and development. For example, Novartis recently struck a wide-ranging partnership (worth up to $1 billion) to use a generative AI platform for designing new protein-based therapies — aiming to accelerate the search for novel disease treatments. GSK has also embraced AI to speed up R&D: its CEO noted that AI modelling helped cut two years off an RSV vaccine trial by predicting where outbreaks would occur and optimising trial site selection. This led to the faster development of the world’s first RSV vaccine, an essential public health breakthrough.

Beyond pharma, medical technology firms are integrating AI into devices, from smart wearables that flag irregular heart rhythms to imaging systems where AI assists in analysing scans for early signs of cancer. Startups and tech companies are introducing AI-driven health apps and chatbots (such as symptom checkers and mental health conversational agents), which some health services in Europe are trialling for patient triage and support. These real-world examples underscore that AI is already deeply enmeshed in the health ecosystem — from global disease surveillance networks to hospital wards and R&D labs — delivering innovations that could improve population health outcomes.

Practicalities and Implementation Challenges

While the potential is immense, implementing AI in public health is a pragmatic challenge. Infrastructure and data interoperability are foundational hurdles. Effective AI requires robust digital infrastructure — high-quality data streams, electronic health records, and cloud computing capacity — which many health systems lack, especially in low-resource settings. Data needed for public health AI often reside in silos or incompatible formats across hospitals, labs, and agencies. Poor interoperability means AI tools struggle to aggregate and interpret information from disparate sources. Bridging these gaps will require significant investment in health information systems, common data standards, and connectivity. Encouragingly, current AI technology can assist in standardising and mapping messy health datasets to make them more usable. Nonetheless, without reliable infrastructure and data-sharing frameworks, even the best AI algorithms cannot deliver consistent results across a public health network.

A related challenge is data quality and representativeness. AI models are only as good as the data they learn from, and health data can be incomplete, biased, or unrepresentative of specific populations. Studies highlight issues like variability in how data are recorded, large amounts of unstructured text, missing information, and coverage bias (e.g. most training data coming from high-income populations).

These factors can undermine an AI system’s accuracy and value to end users. Developing good AI for health requires carefully cleaning and curating data to reflect clinical reality. For instance, algorithms trained only on European hospital data may perform poorly in rural African communities. Implementers must thus invest effort in data preparation and continuously monitor model outputs for anomalies. Establishing metadata standards, common terminologies, and data quality metrics can facilitate better AI development. Additionally, clarity on data ownership and governance is needed: questions about who “owns” health data (patients, providers, governments?) affect how data can be integrated for AI. Resolving these issues through policies and trust frameworks is key to unlocking data for public health AI while respecting privacy and rights.

Another practical consideration is integrating AI tools into healthcare workflows and gaining workforce acceptance. Introducing AI decision-support systems or automation in clinics requires adapting processes and training staff. Health workers may be understandably cautious — some lack familiarity with AI, worry about accuracy, or fear being displaced. Clear protocols are needed if an AI system’s recommendation conflicts with clinical judgment. Early experience shows that human-AI collaboration works best when AI is framed as an assistive tool rather than a professional replacement. Building trust among the workforce involves providing explainable outputs and demonstrating reliability in pilot phases. It also means training clinicians in basic AI concepts and ensuring they feel confident interpreting AI outputs.

Successful deployments (like the PATH TB screening program) emphasise that significant workflow integration and training efforts are required. In that program, implementers had to solve issues of installing the software in clinics, securing internet connectivity for the AI, and ensuring staff could effectively use the AI results within their screening workflow. Without such groundwork, even a high-performing algorithm might sit on the shelf unused. Thus, the human element is crucial: public health organisations must engage and educate their workforce, adjusting roles and processes so that AI enhances rather than disrupts care delivery. Over time, as clinicians see AI reducing drudgery (e.g. auto-filling forms) and improving outcomes, their acceptance tends to grow. Indeed, physician enthusiasm for health AI has been rising year-on-year. Patience and iterative refinement are needed to blend AI smoothly into the complex fabric of health systems.

Risks and Concerns of AI in Public Health

Despite the optimism, it is vital to acknowledge the risks and potential harms associated with AI in public health. Data privacy and security tops the list of concerns. AI systems often require large datasets of patient information, raising the stakes for protecting sensitive personal health data. Any breach or misuse of such data can erode public trust and violate individuals’ rights. There is also the risk of “function creep”, where data collected for health purposes might be used in other ways (for example, a COVID-19 contact tracing app’s data later being used for law enforcement — a scenario that drew criticism in some countries). Moreover, complex AI models could inadvertently leak private details — for instance, a model might be reverse-engineered to reveal records it was trained on. Ensuring robust cybersecurity and strict data governance is therefore paramount. Many call for comprehensive privacy safeguards and compliance with regulations like Europe’s GDPR whenever AI handles health data. Techniques such as anonymisation or synthetic data can help, but they are not foolproof (even de-identified data can sometimes be unidentified).

The bottom line: without public confidence that AI will maintain confidentiality and data security, its benefits will be lost. Public health agencies must be transparent about what data are used and how to obtain informed consent where appropriate and implement state-of-the-art security measures to prevent breaches. Privacy isn’t just a legal box to tick — it’s fundamental to preserving the trust on which public health interventions depend.

Another significant risk is algorithmic bias and the exacerbation of health inequalities. AI systems can unintentionally perpetuate or even worsen disparities if their design is not carefully managed. This was starkly illustrated by a widely used healthcare risk algorithm in the United States that was found to be racially biased. The algorithm helped determine access to extra care programs and used healthcare cost as a proxy for need. This choice systematically underestimated the needs of Black patients (who often had lower healthcare expenditures due to access barriers). As a result, many high-risk Black patients were less likely to be flagged for additional care, denying them the resources they needed. This example shows how bias in data or design can translate into inequitable outcomes: the AI effectively discriminates against a vulnerable group. Similar issues could arise in public health if an AI model is trained on predominantly male patients under-detect conditions in women or if disease surveillance AI better covers wealthier communities with more data. AI could widen gaps if not addressed, with marginalised populations benefiting the least or even being harmed.

Equity must be a central design principle to counter this: datasets should be diverse and inclusive, algorithms should be tested for bias, and bias mitigation strategies (like reweighing data or algorithmic fairness adjustments) should be applied. The WHO explicitly highlights inclusiveness and equity as core ethical principles for AI, ensuring that AI tools work for all segments of society regardless of race, gender, income, or other characteristics. Ultimately, careful governance and auditing of AI systems are needed to avoid encoding systemic biases into digital form and instead use AI to reduce health inequities (for example, by targeting interventions to underserved areas).

A further concern is the lack of transparency (“black box” issue) and its impact on trust and safety. Many AI models, especially deep learning networks, operate as complex black boxes — they do not explain their reasoning in human-understandable terms. In healthcare, this opacity is problematic. Clinicians and public health decision-makers are wary of acting based on a recommendation they don’t understand, particularly if an AI’s advice contradicts intuition or standard practice. Unexplainable AI can also undermine accountability: if an AI makes a harmful mistake, it may be unclear why it happened or who is responsible. This lack of transparency feeds directly into trust issues among professionals and the public. If people perceive AI as a mysterious, untrustworthy “magic wand” imposed on health decisions, they may reject its use. There have been cautionary tales: an AI system deployed in hospitals to predict which COVID-19 patients would need ICU care was later found to underperform because it hadn’t been adequately validated. Clinicians grew sceptical of its risk scores.

To prevent such scenarios, experts call for explainable and interpretable AI in health — algorithms that can provide reasons for their predictions or use transparent, logical rules where possible. At a minimum, users should have access to information about how an AI was developed and its known limitations. Regulatory frameworks like the EU AI Act are likely to mandate a degree of transparency for high-risk AI (including many medical applications) precisely to bolster trust and enable oversight. Building more explainability into AI models remains a technical challenge, but one that is essential for aligning with the principles of transparency and accountability in healthcare.

In the age of ChatGPT and generative AI, misinformation and “AI hallucinations” have emerged as new public health risks. Advanced chatbots can produce remarkably human-like answers to questions — but they do not guarantee factual accuracy. They can hallucinate false information, confidently output incorrect medical advice, nonexistent statistics, or even fake health news. The potential for harm is considerable if the public uses such tools for health information. There is concern that AI chatbots could magnify the health misinformation problem exponentially — for instance, by generating convincing anti-vaccine narratives or spurious cures, which then spread on social media.

In recent years, public health agencies have struggled to combat misinformation (for example, false claims about vaccines or COVID-19 treatments that undermine uptake). The rise of AI-driven content generators and deepfakes only fuels this fire. Misinformation undermines public trust and can lead people to reject proven interventions in favour of dangerous alternatives. Tackling this will require new strategies — such as watermarking AI-generated content, strengthening content moderation, and improving digital health literacy so the public can better discern credible information. On the flip side, public health communicators might also leverage AI to fight misinformation (for example, using AI to detect false rumours early or personalise accurate health messages). Regardless, the advent of easy, AI-generated disinformation is a serious risk factor that the global health community cannot ignore.

Finally, there is the risk of over-reliance and systemic dependency on AI. If health systems come to depend on AI for critical functions without adequate safeguards, any failures in the technology could have severe consequences. For example, an AI model might perform well in normal conditions but fail to generalise during an unexpected scenario. If everyone has come to rely on its output, they may miss the warning signs until too late. Moreover, heavy reliance on automation might erode human skills over time (a phenomenon observed in other industries). In healthcare, this raises concerns about “deskilling” — clinicians might lose practice in specific tasks (like reading x-rays or making complex diagnoses) if those are always handled by AI, leaving them less prepared to step in when needed.

Over-reliance can also dull vigilance: users might stop double-checking results if an algorithm usually works well so that an undetected error could propagate. The key is to maintain a human-in-the-loop approach: AI should support, not replace, human expertise. Mechanisms for human review of AI outputs and fallback plans in case of system outages are essential.

Additionally, performing regular audits and updates of AI models can prevent performance from degrading unnoticed. In summary, while AI can increase efficiency, public health systems must guard against blindly relying on algorithms. A balanced approach that values human judgment and institutional memory, alongside AI’s computational power, will be safest in the long run.

Ethical and Regulatory Frameworks

Addressing the above risks requires robust ethical guidelines and regulatory oversight for AI in health. Globally, there is growing consensus on core ethical principles that should govern AI development and use in public health. The World Health Organization’s landmark 2021 report laid out six guiding principles for ethical AI in health: (1) Protect human autonomy — humans should remain in control of health decisions, with informed consent and respect for privacy; (2) Promote human well-being and safety — AI must be safe, effective, and designed to improve health outcomes; (3) Ensure transparency, explainability and intelligibility — stakeholders should have sufficient information about how AI systems work and decisions should be traceable; (4) Foster responsibility and accountability — developers and users are accountable for AI behaviour, and mechanisms for redress must exist; (5) Ensure inclusiveness and equity — AI should benefit all groups, enhancing fairness and not amplifying disparities; and (6) Promote AI that is responsive and sustainable — meaning AI should be adaptable, monitored, and designed for long-term societal benefit.

These principles, while high-level, provide a value framework to guide everything from design choices (e.g. using diverse training data to ensure equity) to deployment (e.g. always keeping a human in the loop to protect autonomy). Public health organisations are increasingly adopting such ethical frameworks. For instance, the WHO urges that AI deployments be accompanied by community engagement, training for health workers, and continuous evaluation to ensure technologies remain aligned with the public interest. The ethos is straightforward: AI must be people-centred and uphold human rights. Ethics committees or advisory boards can help oversee AI projects, reviewing them for compliance with these principles before they scale up.

On the regulatory front, governments are now moving to establish formal rules for AI in healthcare. The European Union’s AI Act is a pioneering example of comprehensive regulation. Passed in 2024, the EU AI Act takes a risk-based approach, classifying AI systems by risk level and imposing requirements accordingly. Health-related AI is generally deemed “high-risk” under this law, given its potential impact on people’s lives and rights. High-risk AI systems (including most AI used for medical diagnostics, decision support, or resource allocation in health) will face strict obligations. These include rigorous standards for transparency, risk management, and human oversight. For instance, developers of a clinical AI tool must implement a quality management system, ensure their model is trained on appropriate data, and provide documentation detailing the AI’s function and limitations. They must also conduct risk assessments and put in place human oversight measures to prevent automation bias. Notably, the EU AI Act doesn’t just apply to creators of AI — it also holds deployers (such as hospitals or public health agencies) accountable for the safe use of AI.

Health providers must monitor AI system performance, keep logs, and retain ultimate responsibility for decisions (clinicians must have the authority to override AI recommendations if needed). These provisions aim to ensure that human accountability and patient safety remain paramount even as AI becomes embedded in care delivery. Additionally, the Act has a broad reach: any AI system impacting people in Europe must comply, even if developed elsewhere. This could set an effective global benchmark as companies worldwide adjust their practices to meet the EU’s requirements.

Other jurisdictions are also crafting guidelines. The United States, through the FDA, has been evolving its regulatory approach for AI/ML-based medical devices, focusing on premarket evaluation and the idea of “continuously learning” algorithms needing ongoing monitoring. International bodies like the WHO have issued guidance and urged governance innovation, suggesting that governments update regulations to cover AI, establish certification processes, and possibly create registries of approved AI health products. We also see emerging governance models such as algorithmic impact assessments (to evaluate a health AI system’s potential societal impact before deployment) and independent reviewers’ bias audits. In some health systems, procurement of AI now requires meeting ethical checklists or obtaining approval from institutional review boards, similar to new medical interventions.

These steps are part of building a “responsible innovation” culture around AI, encouraging experimentation and advancement, but within guardrails that protect individuals and communities. Multi-stakeholder collaboration is key here — regulators, technologists, health professionals, and patient representatives need to work together to define safe and effective AI in practice and update those definitions as the technology evolves. As one example, the NHS AI Lab in the UK partnered with regulators to create a sandbox for AI developers, guiding them on navigating regulatory pathways and using synthetic data for testing. Such efforts show that with thoughtful governance, innovation and safety can advance hand in hand.

Future Directions and Recommendations

To fully realise AI’s promise in public health while minimising its downsides, several changes and strategic efforts are needed going forward:

• Investing in data and digital infrastructure: Health systems, especially in low- and middle-income countries, need support to build the data foundations for AI. This means digitising health records, improving data quality, and ensuring platform interoperability. Governments and global donors should prioritise funding for health information systems and broadband connectivity as part of public health capacity building. Better data infrastructure not only enables AI — it strengthens health systems overall. Innovative approaches like federated learning (where AI models train on distributed data without moving it) could be scaled to allow resource-constrained regions to benefit from AI insights without breaching privacy. The goal is to create a world where data flows securely and efficiently to wherever it can improve health outcomes.
• Strengthening workforce capacity and AI literacy: As AI becomes a standard tool, public health and healthcare workers must be equipped to use and oversee it. Training programmes are needed to raise AI literacy among the health workforce, including understanding AI’s capabilities and limitations. This may involve updating medical and public health curricula to cover data science basics. Additionally, new specialist roles (such as clinical AI safety officers or epidemiologists with AI expertise) could be developed to bridge the gap between tech and health domains. Frontline staff should be engaged in co-designing AI solutions so that tools are user-friendly and address actual pain points. When health workers understand and trust AI, they can become champions for its adoption and serve as critical watchdogs who notice when something isn’t right. Fostering a culture of continuous human oversight and feedback will ensure that AI remains a servant to health professionals, not a black box dictator.
• Ensuring inclusivity and equity in AI advancement: The global health community must actively work to prevent a digital divide in AI. Much cutting-edge AI development is concentrated in wealthier countries and tech companies. Deliberate efforts are needed to include researchers and perspectives from low- and middle-income countries in AI design so that solutions address diverse needs. This could consist of research funding earmarked for LMIC-led AI projects, technology transfer programs, and south-south collaboration on AI for health. Moreover, data from underrepresented populations should be collected (with consent and protection) to improve algorithms’ relevance in those settings. By democratising AI knowledge and resources, we can avoid a scenario where only certain countries or communities benefit from AI while others are left behind or subject to unchecked harm. Equity considerations should also extend to gender, age, and other demographics — for instance, ensuring women and minority groups are included in AI development teams and that tools serve users of different languages and literacy levels. An inclusive approach will make AI tools fairer and enlarge the talent pool working on creative AI solutions for entrenched public health challenges.
• Fostering collaboration between public health and technology sectors: Effective AI in public health sits at the intersection of epidemiology, medicine, data science, and engineering. No single sector can do it alone. We need stronger partnerships: governments linking with academia and tech firms, NGOs working with startups, and international agencies convening multi-sector consortia for global health AI initiatives. Such collaboration can accelerate innovation and ensure that public health priorities guide technological development (and vice versa, that technologists are aware of on-the-ground needs). For example, a partnership between a national health ministry and AI researchers might focus on building an early warning system for malaria outbreaks, combining epidemiological expertise with cutting-edge modelling. A pharmaceutical company could also collaborate with global health organisations to use AI in vaccine R&D for diseases of poverty. These cross-sector collaborations should be underpinned by fair agreements (e.g. around data sharing or intellectual property) so that all parties benefit and trust is maintained. The complexity of health + AI demands breaking down silos. International forums and networks can play a role here, enabling countries to share best practices and lessons learned (e.g. how one country successfully regulated an AI symptom-checker or how another trained health workers on AI). Since pathogens do not respect borders, a collaborative global approach to AI-enhanced public health security is in everyone’s interest.
• Adaptive governance and continuous evaluation: As AI tools roll out, it is critical to monitor their real-world impact and be ready to adjust course. Public health authorities should implement mechanisms to continuously evaluate AI interventions — collecting data on their accuracy, outcomes, and any unintended effects. Are the predictions helping improve disease control? Is a triage algorithm safely directing patients to the right level of care? This requires establishing key performance indicators and perhaps creating independent evaluation units. When problems are identified (such as an AI starting to drift in accuracy due to changes in data), there should be processes to update or pull back the tool until fixes are in place. Regulation must also remain adaptive; rigid rules could stifle innovation or become outdated as technology advances. One idea is regulatory sandboxes where new AI solutions can be tested under supervision, allowing regulators to learn and guidelines to evolve. Governance models should be proactive yet flexible, emphasising learning and iteration. Importantly, communities and civil society should have a voice in evaluating AI in public health — their feedback on whether these tools are culturally acceptable, understandable, and improving services is invaluable. Responsible AI is not a one-time certification but an ongoing commitment to quality and ethics throughout the technology’s lifecycle.

Looking ahead, it is clear that AI will play an expanding role in public health — whether in combating the next pandemic, extending healthcare to remote villages via smart apps, or analysing big data to pinpoint disease drivers. The revolution is already underway, but its trajectory depends on our current choices. With enlightened leadership, adequate safeguards, and inclusive collaboration, AI could usher in significant public health gains — from more efficient health systems to healthier communities worldwide. However, if we ignore the risks — allowing unchecked use, widening inequities, or losing the human touch in care — the potential benefits could unravel, and public trust could be irrevocably lost. The coming years are thus pivotal. Armed with decades of hard-won experience, public health professionals have a key role in steering this journey. By insisting on evidence, equity, transparency, and community engagement, they can ensure that the AI revolution in health truly becomes a boon and not a threat. The opportunity is immense, but so is the responsibility to guide AI’s integration into public health thoughtfully and ethically.

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