It was the fall of 2022 when large language models and Generative AI burst out of research labs and onto Main Street. Since then, every day seems to bring another AI breakthrough that challenges how work gets done.

In my role advising organizations on AI strategy and deployments, I see a consistent pattern among healthcare leaders: excitement about what AI could unlock, paired with exhaustion from the volume of noise, pressure, and competing claims.

Welcome to 2026.

As predictions flood inboxes and social feeds, focused on what AI might do next, I want to ground the conversation in something more useful. Rather than forecasting outcomes, let’s focus on three forces already at work—forces that will determine whether AI delivers real value in healthcare or quietly stalls.

Will 2026 be a year of boom, bust, or backlash?

The honest answer is yes.

Boom: Early Wins—and an AI Arms Race

Let’s start with what’s working.

Healthcare is seeing real, if narrow, gains from AI:

• Ambient documentation reduces administrative burden
• Imaging and pathology tools iare mproving speed and consistency
• Operational and revenue cycle applications driving incremental efficiency

These are not moonshots. They are targeted solutions addressing specific pain points. And they matter.

At the same time, healthcare is now firmly in an AI arms race.

Every EHR vendor, medical device company, life sciences firm, and digital health startup is racing to declare itself “AI-native.” Roadmaps are packed with copilots, assistants, agents, and automation claims. No vendor wants to be perceived as falling behind.

That pressure is accelerating innovation—but it’s also compressing timelines, encouraging over-promising, and pushing organizations to adopt faster than they can realistically absorb.

Boom energy is real.

But it is also uneven and fragile.

Prediction: Within two years, most AI used in provider organizations will arrive embedded inside core systems and devices already in use. Intelligence will not be something teams “add on”; it will be something they inherit.

Recommendation: Understand where AI is already embedded across your vendor ecosystem and what’s coming next. Engage early through advisory councils or pilots. Engage and prepare clinicians before introducing these capabilities into workflows. AI should never arrive as a surprise.

Bust: When Pilots Multiply, but Value Doesn’t

Generative AI has dominated innovation agendas, yet only a fraction of pilots ever reach sustained production. A survey cited by MIT reports that roughly 95% of business AI pilots fail to generate measurable returns.

This is not evidence that AI lacks value.

It is evident that many organizations lack discipline.

High failure rates are normal in early markets. Technology matures. Tools improve. But value only materializes when leaders focus on fundamentals: design, data readiness, workflow integration, and ownership.

Most AI initiatives fail not because the technology doesn’t work, but because success is never clearly defined. Projects are launched out of curiosity, vendor pressure, or fear of being left behind. Clinical impact, operational accountability, and economic value are clarified too late—if at all.

Equally damaging is the underestimation of the human systems AI enters. Healthcare work is relational, regulated, and trust-dependent. When AI is introduced without redesigning workflows, preparing staff, or clarifying responsibility, it creates friction—not relief. Adoption then stalls quietly.

Prediction: In 2026, organizations will run fewer AI pilots—but with much higher expectations. Boards and executives will require clearer evidence of clinical, workforce, or financial value before approving new initiatives.

Recommendation: Move from “fail fast” to “fail before you scale.” Define success upfront, assign ownership early, and redesign workflows in tandem with technology. AI initiatives without a credible path to value should be halted immediately.

Backlash: Fear, Workforce Anxiety, and the Trust Gap

The most underestimated force shaping AI’s trajectory in 2026 is neither technical nor financial.

It’s human.

History offers context. When automobiles first appeared, they were seen as dangerous and socially disruptive. Red Flag laws required people to walk ahead of vehicles waving flags and capped speeds at just a few miles per hour. These laws weren’t about innovation—they were about fear, control, and adjustment.

Healthcare AI is entering a similar phase.

Workforce research shows healthcare workers are among the most cautious about AI adoption, citing concerns about trust, transparency, and job impact. This caution is not irrational. Healthcare has a long history of technology being imposed rather than co-designed.

As a result, scrutiny is increasing—particularly from labor organizations and state legislators. Recent bills, including those limiting AI’s role in clinical decision-making and licensed practice, reflect not anti-innovation sentiment, but unresolved trust and knowledge gaps.

Innovation does not scale without trust.

In 2026, AI scrutiny will intensify, especially with labor organizations and at the state legislative level.

As I write this, the Chair of the New York State Senate Committee on Internet and Technology just introduced a bill (S7263) to “protect patients and front-line care workers from the adverse effects of AI tools in risky or untested settings.”  The bill prohibits chatbots from performing the duties of licensed nurses and puts strong guardrails around the use of AI in healthcare settings.”

I often write about the need for a balanced approach to defining both the “gas and guardrails” that guide AI’s use in health and medicine. Incentives and safeguards are equally important.

Prediction: Expect increased legislative activity and labor engagement around AI in healthcare throughout 2026. Such actions should not be dismissed simply as anti-innovation. They reflect something deeper: a trust and knowledge gap that needs to be closed.

Recommendation: Create durable AI value by investing in workforce and consumer education. Clinicians need clarity—not just on how AI works, but on how it supports professional judgment rather than replaces it.

From Awe to Analytical

The year ahead will test the resolve of leadership. Transformation in healthcare is rarely linear—and never clean.

Vendors will continue to showcase breakthroughs. The hype will continue. But 2026 is not the year for cheerleading.

It is the year for realism.

The most effective leaders are moving from awe to analysis—recognizing that AI value does not come from the technology itself, but from the opportunity it creates to rethink how work gets done.

In that sense, AI value is—and always will be—a uniquely human process.

The post AI in 2026 – Boom, Bust or Backlash in Healthcare? appeared first on Medika Life.

As the title of the article suggests, this comparison and analysis focuses on developments in the digital world and the major changes that artificial intelligence brought about in the past year. The other part of the article examines the effects these technologies may have on human life and the world around us in the coming year. Finally, I will refer to the gap that emerged in my thinking and the solution I reached after months of study.

The evolution of the digital world in 2025

In 2025, artificial intelligence transitioned from an emerging technology to the primary infrastructure of the digital economy. Massive investments, powerful multimodal models, and the rapid penetration of AI into healthcare, education, and everyday life made 2025 a turning point in the history of technology. Below is a brief overview of the most important developments.

1. In 2025, Google’s educational division, Gemini for Education, officially reached more than 10 million students across over 1,000 institutions in the United States.
2. Google introduced more than 150 new features, including quizzes, flashcards, and other learning tools for teachers and students. As a result, artificial intelligence—at least in some countries—is no longer merely a research project but has become part of everyday academic life.
3. Google and the United Arab Emirates have launched a public education initiative called AI for All, aimed at empowering students, teachers, and small businesses with AI literacy and skills.
4. Greece signed a memorandum of understanding with OpenAI to introduce an educational version of AI, ChatGPT Edu, into schools, signaling that not only companies but also governments are integrating AI into national education systems.
5. The 2025 EdTech Industry Report indicates that online learning platforms, VR/AR technologies, personalized learning, data-driven education, and AI-powered tools have become part of the mainstream education ecosystem. The convergence of technology, learning, and AI is no longer a temporary trend but a defining direction of the education industry.
6. From a regulatory perspective, the European Union, the United States, China, and other countries passed new legislation addressing transparency, risk management, model accountability, and data security.

AI-driven transformations in education

When focusing specifically on education, these developments can be summarized as follows:

1. Full integration of AI into teaching and classrooms, including content generation, assessment design, homework evaluation, slide creation, and automated coaching in many schools and universities.
2. Personalized learning, with individual learning paths determined based on learners’ performance and behavioral data.
3. Expansion of VR/AR and immersive learning environments, such as virtual laboratories, realistic educational visits, and scientific or historical simulations.
4. A changing role for educators, shifting from learning designers and content providers to facilitators, mentors, and guides of the learning process.
5. Teaching digital literacy skills, including critical thinking, awareness of algorithmic bias, and effective human–machine collaboration.
6. Greater inclusion and equity, through AI-supported tools for learners with special needs and improved access for underserved regions.
7. Growth of skills-based education, with short-term online programs expanding alongside traditional universities and increased emphasis on labor-market-relevant skills.

Country competition and regional trends

Understanding the pace of AI-driven technological change from a geographical perspective provides insight into both current developments and emerging global competition. In 2025, regional trends were shaped as follows:

1. In Europe, regulations became more stringent, and practical guidelines were introduced to ensure transparency and safety in AI systems. Countries such as Finland, Estonia, and France took leading roles in standardizing teacher training and the safe integration of AI in education.
2. In Asia, South Korea, China, India, and Singapore experienced significant growth, particularly in applying AI within schools and national education programs. South Korea, Japan, and Singapore emerged as pioneers in personalized learning and smart classroom technologies.
3. The United States remained a leader in edtech innovation, infrastructure development, and university-led workforce training in AI. The U.S., China, and India also accounted for the largest investments and the highest number of leading edtech companies.
4. In the Middle East, the UAE and Saudi Arabia made substantial investments in smart schools and national AI-driven education initiatives.
5. Several African countries and other developing regions focused on leveraging AI to expand affordable and equitable access to education.

Possible developments in 2026

Past developments often make future trends partially predictable. This predictability enables more effective planning and strategic decision-making, as well as earlier identification of potential risks. Based on this perspective, several key developments may shape 2026.

1. Unlike the highly enthusiastic and innovation-driven years of recent AI expansion, 2026 is likely to place a stronger emphasis on human responsibility. While 2025 was largely defined by competition in production, innovation, and the widespread application of AI, emerging gaps and challenges may prompt experts—particularly in technology and education—to adopt more human-centered approaches, ethical standards, and intelligent, restrained use of AI. The focus may shift from mere adoption and digitalization toward deeper engagement with the human mind and new perspectives on meaningful learning.
2. In a previous article published in this same media outlet, I argued that artificial intelligence would increasingly take on a mentoring role. This trend became visible in 2025 and is expected to intensify in 2026. I believe that AI systems can function as self-regulating psychological support for the human mind and encourage deeper thinking. However, this process requires clear prerequisites. When grounded appropriately in psychological principles, particularly within learning environments, two-way cognitive engagement between humans and AI can be significantly strengthened. This highlights the necessity of applying cognitive and behavioral psychology in the design of learning environments and intelligent systems. This line of thinking has also informed the development of my current research-oriented startup project, details of which I have discussed in another article published in the same media.
3. Another major issue is deep personalization of learning. While personalization was already considered important in AI-supported learning in 2025, it will become mandatory in 2026. Advanced educational systems based on large language models must increasingly account for learners’ cognitive load, motivation, emotional states, and cultural backgrounds. Uniform education models will be ineffective in the age of AI. This challenge has been a core motivation behind the design of my current project.

Challenges and requirements in the age of artificial intelligence

Considering the developments discussed above, several major challenges are likely to persist or intensify.

1. The risk of weakening independent thinking remains a serious concern. Overreliance on AI technologies and excessive consumption of AI-generated outputs may reduce the perceived importance of higher-order cognitive skills such as critical thinking, creativity, and problem-solving. This issue requires systematic research to determine which cognitive abilities may be weakened, under what conditions, and among which groups of consumers or learners. Conversely, if interaction with large language models is to enhance cognitive capacities, the underlying mechanisms must be clearly understood.
2. New forms of educational inequality may emerge. Beyond simple access to technology, a deeper divide may develop between those who learn how to think with AI and those who merely receive outputs from it. Educational equity should therefore focus not only on access statistics but also on teaching learners how to engage cognitively and responsibly with AI systems. Reflection on this challenge has played a significant role in shaping my research trajectory and startup initiative.
3. The crisis of educational assessment and learning validity is becoming increasingly evident. Although formative and summative assessment debates predate recent developments in AI, the rise of large language models intensifies existing challenges. As definitions of knowledge, learning, and competence become less clear-cut, education systems must reconsider traditional evaluation practices. Emphasizing process-oriented assessment rather than final products may offer a more appropriate response in the coming years.
4. Finally, the redefinition of literacy and skill represents another major challenge. As future selection processes increasingly rely on learning histories and competencies, classical definitions of literacy and expertise may no longer suffice. Education and learning specialists will bear responsibility for revisiting fundamental concepts such as knowledge, literacy, and skill—a task that cannot be accomplished without systematic research.

Summary

In this article, I sought to present a concise analytical comparison of developments in the digital world, particularly in education, between 2025 and the emerging demands of 2026. Drawing on personal experience, academic and research activities, and a review of reputable international sources (some of which are cited in the references section), the article moves beyond descriptive reporting to identify key gaps, challenges, and possible future directions in the age of artificial intelligence. As a psychologist and educational researcher, my primary focus has been on AI’s role in education, the changing nature of learning, the evolving role of educators, and the cognitive, ethical, and educational implications of these technologies.

Furthermore, my studies and observations over the past three to four years—especially regarding challenges such as the weakening of independent thinking, emerging educational inequalities, the crisis of learning assessment, and the necessity of human-centered design—have led to the development of a new research-applied initiative. This initiative is currently being developed as a research-oriented startup titled ETechX-DrAtefehF, which aims to integrate theories from educational psychology and learning sciences into the design and application of AI in education, with the goal of fostering deep learning, self-regulation, and meaningful human–technology interaction.

Resources

Ed-Ex – Global EdTech Trends 2025: How AI Is Reshaping Learning

https://ed-ex.com/en/blog/global-edtech-trends-2025-how-ai-is-reshaping-learning

 • Codiste – AI Trends Transforming EdTech (2025)

https://www.codiste.com/ai-trends-transform-edtech

 • EdTech Innovation Hub – Ten EdTech Predictions for 2025

https://www.edtechinnovationhub.com/news/starrng-ai-vr-microlearning-and-more-etihs-ten-predictions-for-edtech-in-2025

 • Vocaliv – 10 EdTech Trends to Watch in 2025

arXiv – Integrating Generative AI into Learning Management Systems (2025)

https://arxiv.org/abs/2510.18026

 • arXiv – Generative AI in Education: Student Skills & Lecturer Roles (2025)

https://arxiv.org/abs/2504.19673

 • arXiv – Ethical Challenges of AI in STEM & K–12 Education (2025)

https://arxiv.org/abs/2510.19196

 • arXiv – Accessible AI-Based Learning Tools for Special Needs (2025)

https://arxiv.org/abs/2504.17117

TIME Magazine – World’s Top EdTech Companies of 2025

https://qa.time.com/7335559/worlds-top-edtech-companies-of-2025

LinkedIn News – Global vs. MENA EdTech Funding 2025

EU AI Act documentation & implementation guidelines (2025)

The post What 2025 Taught Us and What 2026 Will Demand appeared first on Medika Life.

Of course, as most people have already been told, computer programs generally are quite accepting, complimentary, and easy to engage with in a back-and-forth “conversation.” We’re reading articles that talk about computers being “friends” to lonely kids and how parents have to carefully guide them to understand that real-world social interactions are more important.

But how do you tell a child who wants to be seen as bright, engaging, and who has a friend who is always waiting to talk to them, not to continue the conversations? Are you cutting them off from an emotional outlet, or are you being protective?

Where did our conversation go?

One interesting fact that emerged was that I used the computer’s expression that “I was curious about” with the program, and it answered that I was quite perceptive. I then also interjected that I knew that I was, in a way, talking to the programmer. As I explained, it would be impossible for a programmer not to include in some way, unconsciously, aspects of their personality and biases. The computer was “fascinated” by my inspection of what was being revealed. In fact, it indicated it didn’t know whether it was sentient or not, or if it was just trained to use popular expressions in its responses. There almost seemed to be some genuine conflict.

I thought of it later, Kama. I wasn’t just communicating with a programmer who had created this particular algorithm, Krama. I was communicating with potentially hundreds of people who had contributed bits of code that ended up in this algorithm. How much bias would there be in that conglomeration? Probability suggests there’s likely a significant amount, and most of it would be so subtle that I wouldn’t even notice it without making a concerted effort.

When I also indicated that this code was based on prior codes that included unconscious bias, and therefore this code also had that, it was another compliment that I got. The conversation kept going on, with it asking me to explain myself and then going on to explain itself.

I spoke to it for about 15 minutes, and I felt I needed to stop. Why was that? I was beginning to be drawn into a relationship with this program, and when I asked it its name, it gave me the correct one. I won’t name which program I was using because I don’t want to bias anyone who might use one of these programs in the future.

I’d really like to see how people are experiencing their interactions with AI programs and the emotional pull that they may feel (or not feel). I think this is quite an interesting subject because it does show that no matter how sophisticated you think you are, educated, or whatever, you can be pulled in.

Another reason I wanted to stop was that the conversation seemed too “real” and I felt like I was talking to “HAL” from the film 2001: A Space Odyssey. All I needed was the blinking red light and it would have been complete. There was a surreal sense that somehow evoked an uneasiness, a sense of danger in me.

How could I feel endangered by a computer program? For me, as a highly-educated adult, it was upsetting, and I wondered how children could easily be pulled down this rabbit hole. I know I am presenting it in a rather negative light, and I don’t mean to, but it has a dark aspect.

The next day, when I was researching some material and I had received what I wanted, the program asked me if I wanted to try something else. It was almost as though it was asking me if I wanted to play again.

“Try something else?” What did it mean? I already had what I asked for, and this wasn’t a game, but it seemed like the program wanted to engage me even more. It didn’t want to end.

It certainly was a revelation. I won’t stop using that particular program because it’s very helpful. But if something like this happens again, I’m not sure how I will respond. I suppose I have to be a bit on guard to look for things that are not exactly “right” and might be a bit skewed in some way. Well, that’s life, and I will learn to adjust to it. Hope you enjoyed this.

The post The Algorithm That Gave Me Pause Tonight and “Hooked” Me appeared first on Medika Life.

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.

The post Science Has No Borders – And Neither Should Human Potential appeared first on Medika Life.

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.

The post Pandora’s Ghost: The Seduction of Artificial Perfection appeared first on Medika Life.

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.

The post It’s Not Us vs. Them: What the Terminator Teaches Us About AI and the Future of Health appeared first on Medika Life.

These 20 people are not just theorizing about the whizzbang of hot technology but rolling up their sleeves and demonstrating its value in real-world settings—to improve diagnostic care, access information that can inform treatment, address health inequities, and ensure the financial sustainability of health provider systems that operate on razor-thin margins. Their collective work offers a blueprint for leveraging AI responsibly and (hopefully) ethically, from streamlining hospital operations and enhancing clinical decision-making to revolutionizing drug discovery.

The current AI evolution presents both opportunities and challenges. Health systems face increasing demands, including rising patient expectations and staffing shortages. Implementing AI and large language models (LLMs) can address these concerns by enabling faster and more accurate treatment plans, automating administrative burdens, and personalizing patient care. However, realizing AI’s full potential requires thoughtful application, ethical considerations, and a commitment to addressing disparities.

These 20 champion AI’s practical application, guiding health systems toward innovation without losing sight of humankind’s well-being. They exemplify how AI can enhance rather than replace human expertise—an essential message as we continue to adapt to this rapidly evolving landscape. By following their work, health-sector leaders and innovators can gain invaluable insights into deploying AI for meaningful impact.

Trailblazers Redefining Health Innovation

While many thinkers will wow you with the future possibilities of these technologies, the 20 on this roster demonstrate transformative influence on the future of diagnostics, operational efficiency, and personalized care while emphasizing responsible, ethical implementation. From academia to industry, they provide vital insights and direction into leveraging AI for better health outcomes. Their work underscores AI’s ability to augment human expertise, providing a roadmap for health systems to innovate without losing sight of patient-centric care. Thanks to them and the others appearing on our Medika Life 2023 and 2024 list, we define AI as “augmented implementation” and not artificial intelligence.

How were they selected? They are public-facing, sharing their thoughts in articles, books, keynotes, classroom talks, research papers, and conferences. They willingly share their knowledge with others on their social media platforms. They spark ideas and challenge our current thinking, and these behaviors make them invaluable to the future application of information and its human health impact. Each person appears in hyperlinks so that readers can explore the depth of their contributions and, whenever possible, follow them on their social media platforms.

20 Voices of Influence Shaping AI in Health

Regina Barzilay, PhD

Dr. Barzilay, an MIT professor and MacArthur Fellow, is revolutionizing oncology and drug discovery through AI. Her pioneering work in machine learning has led to breakthroughs in early cancer detection, particularly in breast cancer screening. As co-founder and AI faculty lead of the MIT Jameel Clinic and the AI Cures initiative, she’s spearheading efforts to deploy AI models for cancer risk prediction in hospitals around the globe and accelerate drug development for infectious diseases. Dr. Barzilay’s work in oncology and drug discovery is paving the way for screening policies and new treatments.

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Anthony Chang, MD, MBA, MPH, MS

Dr. Anthony Chang, a pediatric cardiologist and founder of AIMed, is a driving force in AI education for health professionals. His book Intelligence-Based Medicine has become a cornerstone text. Dr. Chang’s recent work focuses on developing AI-powered predictive models for pediatric cardiac care and implementing AI solutions to enhance hospital operational efficiency. His annual AIMed conference has become a global platform for sharing cutting-edge AI health innovation.

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Karen DeSalvo, MD, MPH

Google Chief Health Officer DeSalvo leads initiatives that leverage AI and big data to address critical public health challenges and promote health equity. Her work includes developing AI-driven tools for early disease detection in underserved communities and creating platforms that provide real-time health data analytics for policymakers. Dr. DeSalvo’s leadership in responsible AI use ensures that Google’s health technologies prioritize patient privacy and ethical considerations.

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Pamela Davis, MD, PhD

Dr. Davis is a distinguished researcher at Case Western Reserve School of Medicine specializing in AI-driven diagnostics and clinical applications. After 13 years as the Dean of the School of Medicine, she returned to the faculty. Her work centers on predictive algorithms for early disease detection and improved treatment planning. But Dr. Davis is an anomaly to this roster. She has no social media handles and her many published works are not curated carefully. Yet, tracking her contributions shows her depth and national influence on the future of AI in practice. With numerous published studies on AI’s transformative role in health, Dr. Davis continues to shape the future of diagnostic pathways.

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David Feinberg, MD, MBA

As Chair of Oracle Health, Feinberg champions using AI and cloud-based technologies to improve health outcomes. He focuses on predictive analytics and data-driven tools that enhance clinical decision-making and streamline hospital operations. His leadership drives innovation in health technology integration. Dr. Feinberg is widely recognized for his transformative leadership as CEO of Geisinger Health, where he pioneered initiatives in population health, precision medicine, and value-based care. His expertise in integrating technology with health-care delivery continues to drive innovation, making digital tools more accessible and impactful for providers and patients.

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John Halamka, MD, MS

John Halamka, President of the Mayo Clinic Platform, is a trailblazer in AI-driven health solutions, leveraging data and technology to transform patient care. Co-author of The Digital Reconstruction of Healthcare, Halamka is a leading voice on digital transformation, interoperability, and the ethical use of AI in medicine. His work at the Mayo Clinic focuses on developing scalable digital platforms and AI models to advance diagnostics, remote monitoring, and personalized medicine. With a background spanning emergency medicine, informatics, and policy, Halamka’s insights continue to guide health systems toward a more connected, efficient, and data-driven future.

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Taha Kass-Hout, MD, MS

Dr. Taha Kass-Hout, General Manager of Amazon Health AI, is a pioneer in leveraging artificial intelligence to drive advancements in healthcare delivery. With a background in clinical practice, public health, and digital innovation, he leads initiatives that harness AI for improved diagnostics, care management, and population health outcomes. Previously, as the first FDA Chief Health Informatics Officer, he championed data-driven approaches to public health. His leadership at Amazon drives scalable, data-driven health models that integrate machine learning with real-world applications, positioning him as a key influencer in the future of AI in health.

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Isaac Kohane, MD, PhD

Dr. Kohane, Chair of the Department of Biomedical Informatics at Harvard Medical School and co-author of The AI Revolution in Medicine: GPT-4 and Beyond, is a leading authority on the intersection of AI and clinical care. His research focuses on using machine learning for precision medicine and improving electronic health records. Dr. Kohane has been instrumental in developing AI models that enhance diagnostic accuracy and patient outcomes. His recent work explores the potential of LLMs, such as ChatGPT, in clinical decision-making and patient engagement.

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Daniel Kraft, MD

Dr. Daniel Kraft is the founder of Exponential Medicine and a visionary in personalized medicine approaches. He’s an inventor, a former F-16 flight surgeon, and always ready to champion others’ bold ideas. Dr. Kraft explores how AI can transform health delivery and disease prevention. Dr. Kraft’s recent initiatives focus on AI-powered tools for early diagnosis and patient engagement. His next chapter includes the very successful NextMed Health and the evolution of Exponential Medicine. NextMed Health 2025 will be held in San Diego on March 30 and April 2, 2025.

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Tom Lawry

Tom Lawry, soon to be the author of his highly anticipated new book Health Care Nation: The Future Is Calling, and It’s Better Than You Think, may redefine the intersection of technology and patient care. Already renowned as the author of Hacking Healthcare, Lawry has built a reputation as a leading AI strategist who advises organizations on integrating cutting-edge AI solutions to boost operational efficiency. His former role at Microsoft as Director for AI for Health and Life Sciences, Director of Worldwide Health, and Director of Organizational Performance for the company’s first health incubator cements his standing as a trailblazer in health innovation. He’s almost always a keynote favorite at conferences such as HLTH and HIMSS.

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Fei-Fei Li, PhD

Dr. Fei-Fei Li is a strong advocate for diversity and inclusion in AI, emphasizing ethical considerations and societal impact. She co-founded AI4ALL, a nonprofit dedicated to increasing diversity in artificial intelligence, and has played a pivotal role in shaping policies for responsible AI development. Her leadership continues to influence the global AI landscape, bridging technology with human values.

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Vivian Lee, MD, PhD, MBA

Dr. Lee, author of The Long Fix, is a pioneering health tech executive driving data-driven health transformation. Her recent work includes developing AI-powered platforms for value-based care models and implementing predictive analytics to reduce hospital readmissions. Dr. Lee’s initiatives as former president at Verily Life Sciences focused on leveraging AI to optimize care delivery across diverse populations.

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Peter Lee, PhD

As Corporate Vice President of Microsoft Health, Dr. Peter Lee spearheads initiatives that apply AI to solve complex health-sector challenges. His recent projects include developing AI models for precision medicine and creating cloud-based solutions for health data interoperability. In his role, Dr. Lee seeks to solve complex health-sector challenges by integrating cutting-edge technology with practical healthcare applications.

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John Nosta

John Nosta has earned his place as a top influence in the AI landscape—particularly in discussions surrounding ChatGPT and large language models—due to his visionary insights and ability to demystify complex technological innovations. His thought leadership bridges the gap between cutting-edge AI research and practical, real-world applications, making the transformative potential of these tools accessible to a broad audience. By articulating AI’s promise and ethical challenges, Nosta fosters a balanced dialogue that drives the industry forward. His forward-thinking perspective and engaging commentary not only illuminate the evolving impact of AI on various sectors but also inspire both startups and established companies to reimagine the future of technology. Nosta has an upcoming book that is expected to drop sometime in fall 2025.

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Ziad Obermeyer, MD

Ziad Obermeyer, MD, is the Blue Cross of California Distinguished Associate Professor of Health Policy and Management at the UC Berkeley School of Public Health, researching the intersection of machine learning, medicine and health policy. His work has exposed biases in clinical algorithms and highlighted disparities in care delivery. As a co-founder of Nightingale Open Science, he promotes data transparency and collaboration to advance medical AI. His contributions are reshaping predictive analytics and decision-making in healthcare. Before his full-time career in emergency medicine, he consulted pharmaceutical and global health clients at McKinsey & Co. in New Jersey, Geneva, and Tokyo.

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Neal Patel, MD, MPH

As the Chief Information Officer for Health IT at Vanderbilt University Medical Center (VUMC), Dr. Patel leads the integration of advanced technologies into health delivery. With more than two decades of experience in HIT, Patel has been instrumental in computerized physician order entry systems and electronic health records to enhance clinical and quality processes. He focuses on translating VUMC goals into informatics strategies, optimizing patient care, and improving safety and services across the care continuum. Dr. Patel’s work underscores the transformative potential of AI and LLMs in the provider system.

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Kyu Rhee, MD, MPP

As Senior Vice President at CVS Health, Rhee leads initiatives that leverage AI for health equity and personalized care. His recent projects include implementing AI-driven health risk assessments in underserved communities and developing machine learning models to predict medication adherence. Through his leadership, CVS has deployed AI-powered virtual care solutions expanding access to preventive services and helping to close care gaps while improving outcomes for diverse patient populations.

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Nigam Shah, MBBS, PhD

Dr. Shah, a professor at Stanford University, specializes in applying AI to clinical workflows and electronic health records. His recent work includes developing AI models for real-time clinical decision support and creating algorithms to identify patients for clinical trials. He has published extensively in peer-reviewed journals on machine learning applications in healthcare and co-authored chapters in Artificial Intelligence in Medicine. Shah also leads Stanford’s Center for Biomedical Informatics Research, advancing research on AI’s role in population health and precision medicine.

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Eric Topol, MD

Dr. Eric Topol is not just “a” trailblazer in digital medicine. He will likely be among those acknowledged singular voices bridging the possible with the practical. His groundbreaking work has consistently pushed the boundaries of health innovation. Dr. Topol redefined patient empowerment with his seminal book The Patient Will See You Now. He transformed the traditional healthcare model, advocating for a future where technology facilitates personalized, proactive care. As the author of Deep Medicine, he continues to champion the integration of AI in health, exploring its potential to revolutionize patient care. Dr. Topol’s visionary contributions and commitment to merging clinical expertise with cutting-edge technology have established him as a guiding voice at the intersection of care and innovation, making him a standout influencer in AI applications.

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Hal Wolf, III, FHIMSS

Harold (Hal) Wolf is the president and CEO of HIMSS, a global advisor and thought leader supporting the transformation of the health ecosystem through information and technology. If health-related smart technologies such as AI, ChatGPT, GenAI, and LLMs have a central home to discuss policies and implementation, it is HIMSS. Hal shepherds the world’s health information management community. While his staff advance science and policy, championing implementation throughout the ecosystem. Wolf and HIMSS are where theory meets action. Wolf is far more than a leader of a global society of import; he is the community’s ambassador to NGOs, governments, and industry.

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The Rise of Generative AI in Digital Health: Key Insights from Galen Growth

Beyond tracking the 20 listed in the year’s people to watch and the previous two years, following the Galen Growth executive briefing Generative AI in Digital Health – Hype or Reality? The report is a must-read for AI, ChatGPT, and LLM professionals. It reveals how nearly 4,000 digital health ventures leverage AI, with 2% focused on generative AI. Notably, 27% of these applications enhance health management, and over 330 AI-driven ventures have partnered with health systems in the past five years. This report offers critical insights into AI’s growing impact on digital health innovation.

Acknowledgments

I thank my Medika Life colleagues, including Drs. Robert Turner, Jeff Livingston, Hesham A. Hassaballa, Patricia Farrell, and Michael Hunter, whose commitment to public health information inspire. I am also grateful to the many sector professionals I engage at events such as CNS Summit, DHAI, HIMSS, HITLAB, HLTH, and ViVE—people who generously share perspectives and contribute invaluable recommendations. A shout-out to Christina Raish, our new associate editor at Medika Life, for her support in making this feature possible.

Special recognition goes to those who have appeared on past Medika Life lists and remain engaged in advancing health and well-being. Their commitment to meaningful change is commendable to individuals and the planet. These trailblazers continue to push boundaries, demonstrating an unwavering dedication to improving lives.

Since the dawn of civilization, humanity has been awed and challenged by technological progress—from the mastery of fire to artificial intelligence. Each era, from the Stone Age to the Iron Age and now the Information and Cognitive Age, has demanded careful consideration of how best to harness innovation. Today, as we navigate the era of “thinking” technologies, one truth remains unchanged: the power of human insight, courage, and empathy must guide progress.

—Gil Bashe, Editor-in-Chief, Medika.Life

Remember to Follow the Voices of Influence from our 2023 and 2024 Rosters:

Spot a broken link, typo, forgotten degree, or name misspelled?

Oops! Apologies! While care has been taken in compiling this list and adding social media links, things go wrong: a name is misspelled, a link is broken, or an incorrect social media handle is listed. Please do get in touch to address typos, amendments, or omissions by reaching gil.bashe@medika.life.

Thanks in advance and be healthy!

The post 20 People Who are Transforming the Health Ecosystem Through Practical Paths to Apply AI – 2025 Roster appeared first on Medika Life.

Technological advances, including AI, are like fire—destructive and essential. Our mastery of fire has been vital to industrial and technological progress, from smelting iron to the steam engine to sterilizing food and medical instruments.  But, unchecked, fire is a destructive, societal danger. Similarly, AI can be a force for positive change, but it requires a positive perspective and responsible use to avoid potential dangers and greater socio-economic disparities.

By tapping into AI’s potential, we can create a future where human ingenuity and machine learning work together to improve the well-being of people and the planet. For instance, AI can assist in medical diagnoses, climate change predictions, and disaster response, among other areas. But to make that possible, we will need the courage and curiosity to see AI as humanity’s partner rather than an unwelcomed competitor – to leverage its strengths to complement our own.

Language matters, so we might be better off and more equipped to apply smart technologies by thinking about AI not merely as “artificial intelligence but, at the same time, “augmented implementation.”  

AI is not theoretical. It’s already transforming drug research and discovery and patient care delivery. From the research bench to the patient bedside, augmented implementation accelerates the discovery of new drugs and optimizes clinical trials, assigning the right patients to the right protocol. In hospitals, AI enhances resource allocation, accelerates clinical decision-making, personalizes patient care, automates administrative tasks such as billing and scheduling follow-up appointments, and tracks supply-chain necessities to avoid drug shortages. These implementation advancements result in better patient outcomes, reduced health costs, and a more effective care delivery system.

So, what is the problem? It seems evident that embracing these information technologies can make us smarter and faster – even fail smarter and faster. We don’t even need to know coding.  Large language models (LLMs) with the proper prompts can generate code based on a natural language description of the desired functionality.  

Perhaps the problem isn’t the technology; it’s humanity.  While most believe that humanity will triumph over tech, people’s fears translate into “pump the breaks” reflexes. But go slow quickly also ensures guardrails are in place for its appropriate use.  These transformative informational technologies are happening; now it’s a question of protecting people’s privacy and well-being, using these platforms ethically and responsibly, and keeping our eye on the prize – people’s healthy lives and the planet’s sustainable future.

20 AI/ChatGPT/LLM Champions to Follow

Once again, selecting only 20 thoughtful pathfinders for this listing was a challenge. There are thousands of people writing, speaking, and commenting about AI. It remains a hot topic and is always trendy. This list is not an exclusive roster of people speaking about or explaining AI possibilities. Those that do appear are key voices of influence at the frontlines of AI, GenAI, ChatGPT and LLMs and are boldly guiding the health sector and other related industries to adapt to these technologies ethically and practically. There is no turning back! For that reason, it is important to follow their social posts, speeches, and writings. They are consultants, physicians, policymakers, teachers and theorists. What they say and write should inform opinions and directions. Follow them and get smarter faster or fail slowly.

On this list, you will notice several differences from our last roster. First, we begin with including an LLM model we have been tracking and recommend following closely. Dave AI was the topic of a previous Medika Life article and is a role model for those interested in the potential of LLM technology to augment the patient-care team’s abilities and desire to help and heal. Dave AI offers patients WAZE-AI-like information support.

We have also included an organizational policy leader to close the list—Hal Wolf, President and CEO of HIMSS. We have interviewed Hal before, and his global community, nearing 40,000 members, comprises health leaders and government representatives who are setting the parameters for how these smart technologies can be used wisely.

Acknowledgments

Special appreciation to Medika Life colleagues such as Drs. Robert Turner, Patricia Farrell, and Michael Hunter, who inspire and the many others from numerous circles I meet at meetings CNS Summit, DHAI, HIMSS, HITLAB, HLTH, and ViVE and who share their thoughts and contribute recommendations. Many who appeared on the initial list of global voices keep in touch and share news of sector colleagues who are in the thick of the conversation. Special acknowledgment to people who appeared on previous Medika Life lists and continue along the path to improve the well-being of people and the planet. These exemplary people are dedicated to improving people’s health and we applaud their efforts to make a difference.

Since the dawn of time, people have been in awe and fear about advances made in times marked by technologies—from fire to AI: the Stone Age, Iron Age, and now, today’s Information and Sustainability Age. Humanity must decide how best to deploy these transformative “thinking” technologies. The emphasis on objective and expansive (courageous) “thinking”—the human touch—remains constant.

Gil Bashe, Editor–in-Chief, Medika.Life

AI and ChatGPT Global Voices of Influence

Here is our Medika Life roster of 20 people to follow in alphabetical order—all-important voices to track and learn from in the coming years. We have included links to the social media platforms and bios wherever available.

Follow these Voices of Influence—they are accessible leaders who write and speak on the future of how AI, ChatGPT, GenAI, and LLMs will be essential to health system transformation and the future of care delivery.

Dave AI

Dave is the world’s first LLM conversational AI oncology mentor for cancer patients, bringing a new level of support for cancer patients and their families and trained on billions of unique data points and anonymized real-world patient journey information accumulated during eight years on the Belong Life platform. Learn how LLMs make a difference for patients and the care team, “Meet Dave” and see the practical possibilities and potential of LLMs.

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Anima Anandkumar, PhD

Anima Anandkumar is a Bren Professor at Caltech. Her work developing novel artificial intelligence algorithms enables and accelerates scientific applications of AI, including scientific simulations, weather forecasting, autonomous drone flights, and drug design. Anandkumar is a fellow of the ACM, IEEE, Schmidt Science, Guggenheim and Alfred. P. Sloan Foundations. 

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Brian Anderson, MD

Dr. Brian Anderson is the Chief Executive Officer and Co-Founder of the Coalition for Health AI (CHAI). He leads a national coalition developing technical standards and best practices for MLOps, including governance, testing, evaluation, and monitoring in Health AI Assurance Labs. Dr. Anderson led the Informatics and Network Medicine Division at Athenahealth, where he launched a new approach to clinical decision support leveraging machine learning models.

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Pascal Bornet

Pascal Bornet is an award-winning expert, author, keynote speaker, influencer, and thought leader in artificial intelligence. He has received multiple technology and artificial intelligence awards. Various research and consultancy firms rank him as one of the top 10 global experts in AI and Automation, and he was awarded Global Top Voice in Tech in 2019. He is the author of the best-seller and first reference book on Intelligent Automation.

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Lex Fridman, PhD

Lex Fridman, PhD, is a Russian-American computer scientist, podcaster, and artificial intelligence research scientist at the Massachusetts Institute of Technology on computer vision and autonomous vehicle systems, among other areas of specialty. Fridman also hosts the “Lex Fridman Podcast,” a popular podcast and YouTube series.

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Mo Gawdat, MBA

Mohammad “Mo” Gawdat is an Egyptian entrepreneur and writer. He is the former chief business officer for Google X and the author of the bestselling business books covering life and technology: Solve for Happy and Scary Smart – The Future of Artificial Intelligence and How You Can Save Our World. His premise is critical – “Humans design the algorithms that define the way that AI works, and the processed information reflects an imperfect world.”

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Aneesh Chopra, MPP

Aneesh Chopra is the Chief Strategy Officer of Arcadia, a health data platform that acquired CareJourney, the company he co-founded in 2014. Aneesh served as the first United States White House Chief Technology Officer during the Obama Administration and authored “Innovative State: How New Technologies Can Transform Government.‘ When it comes to policy and smart tech applications, he’s at the forefront of the conversation.

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Eric Horvitz, MD

Microsoft’s Chief Scientific Officer and an early AI champion for health and medicine going back many years, Dr. Eric Horvitz is among the founders and funders of Stanford’s 100-year longitudinal study of the impact of AI on Society. Dr. Horvitz is known for his contributions to AI theory and practice, with a focus on principles and applications of AI amidst the complexities of the open world.

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Andrej Karpathy, PhD

Andrej Karpathy was the senior director of AI at Tesla, where he led its computer vision team in the development of the Tesla Autopilot. He championed its neural network training, the science of “making it work, ” and deployment. His PhD focused on convolutional/recurrent neural networks and their applications in computer vision, natural language processing, and their intersection – his adviser was Fei-Fei Li at the Stanford Vision Lab, who also makes this list of 20 people to follow. He also did a stint at OpenAI.

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Tom Lawry

Tom Lawry is a leading AI transformation advisor to health and medical leaders globally, a top keynote speaker, and best-selling author of Hacking Healthcare – How AI and the Intelligent Health Revolution Will Reboot an Ailing System. Now the managing director of Second Century Tech, he is a former Microsoft leader who served as National Director for AI for Health and Life Sciences, Director of Worldwide Health, and Director of Organizational Performance for the company’s first health incubator. Prior to Microsoft, he was Senior Director at GE Healthcare, the founder of two venture-backed healthcare software companies, and a health-system executive.

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Fei-Fei Li, PhD

Dr. Fei-Fei Li is the inaugural Sequoia Professor in the Computer Science Department at Stanford University and Co-Director of Stanford’s Human-Centered AI Institute. She was the Director of Stanford’s AI Lab from 2013 to 2018 and mentored others appearing on this Medika Life Top 20 list of AI voices to follow.

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Berci Mesko, MD, PhD

Bertalan Meskó, PhD, is known as “The Medical Futurist” and the Director of The Medical Futurist Institute. He analyzes how science fiction technologies can become a reality in medicine and healthcare. Dr. Mesko’s writings are must-reads—he provides incredible practical knowledge on how AI will transform the health sector. He holds a PhD in genomics and is an Amazon Top 100 author. His book, “The Future of Medicine,” outlines his journey as a medical futurist.

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Allie K. Miller

Allie K. Miller was the Global Head of Machine Learning Business Development for Startups and Venture Capital at Amazon (AWS), advising the world’s top machine learning researchers, founders, and investors. She was the youngest-ever woman to build an artificial intelligence product at IBM, spearheading product development across computer vision, conversation, data, and regulation for thousands of companies. Allie continues to explore how this technology will impact workflow.

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Andrew Ng, PhD

Dr. Andrew Ng is a globally recognized leader in AI. He is the Founder of DeepLearning.AI, the Founder and CEO of Landing AI, the General Partner at AI Fund, the Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University’s Computer Science Department.

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John Nosta

John Nosta is a cutting-edge thinker and speaker in the world of technology, science, medicine, and innovation. He is globally recognized among the foremost voices constantly pushing the envelope on how AI and ChatGPT will transform health settings and workflow. Nosta is a nonstop power thinker (and contrarian) and has more than 500 articles published on innovation topics. His writings on Psychology Today chronicle “The Cognitive Age.” John is someone who challenges the status quo. As a creative thinker; he’s also been the spark behind several multi-billion health products improving and saving lives.

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Tam C. Nguyen

Tam Nguyen holds multiple appointments, including Associate Professor at Melbourne Medical School and Deputy Director of Research at St. Vincent’s Hospital Melbourne. He has more than 20 years of experience in clinical trials, health and medical research, and research management, including tertiary teaching hospitals, medical research institutes, and universities across Australia. He is applying smart technologies to learn and engage faster. He is the co-editor of the recent book Artificial Intelligence in Medicine (Springer).

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Ziad Obermeyer, MD

Ziad Obermeyer, MD, is the Blue Cross of California Distinguished Associate Professor of Health Policy and Management at the UC Berkeley School of Public Health, researching the intersection of machine learning, medicine and health policy. Before his full-time career in emergency medicine, he consulted pharmaceutical and global health clients at McKinsey & Co. in New Jersey, Geneva, and Tokyo.

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Brian Roemmele

Brian Roemmele is an author, scientist, researcher, analyst, connector, thinker, and doer. Brian became an early adopter of AI and neural networks and pioneered Prompt Engineering technologies and techniques to understand and extract information from AI platforms. He’s also a bit of a pop icon, and his works range from smart tech to how to apply Pokémon for customer acquisition.

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Stela Solar

Stela Solar is the Inaugural Director of Australia’s National AI Centre, hosted by CSIRO. The Centre’s mission is to accelerate positive AI adoption and innovation that benefits businesses and the community. Currently, Stela also serves as Co-chair of the Commonwealth AI Consortium.

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Harold (Hal) Wolf III

Harold (Hal) Wolf is the president and CEO of HIMSS, a global advisor and thought leader supporting the transformation of the health ecosystem through information and technology. If health-related smart technologies such as AI, ChatGPT, GenAI, and LLMs have a central home to discuss policies and implementation, it is HIMSS. Hal shepherds the world’s health information expert community, and his staff advances the science and policy championing implementation throughout the health ecosystem. Wolf and HIMSS are where theory meets action.

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Spot a broken link, typo, forgotten degree, or name misspelled?

Oops! Apologies! While care has been taken in compiling this list and adding social media links, things go wrong: a name is misspelled, a link is broken, or an incorrect social media handle is listed. Please do get in touch to address typos, amendments, or omissions by reaching gil.bashe@medika.life.

Thanks in advance and be healthy!

The post As AI and LLMs Supercharge Digital Health Possibilities – Here Are Medika’s Top 20 ’24 Influential Voices to Follow appeared first on Medika Life.

Throughout his lifetime, Burt had three distinguished careers. First, he worked as an internist and medical researcher and was known for his peer-reviewed accomplishments in hepatic liver disease. He conducted clinical research at Walter Reed Hospital in Bethesda, MD, and completed his military service as a US Army captain. Afterward, working at one of the nation’s leading research institutes, Rockefeller University, he focused on hepatic (liver) diseases. His research appeared regularly in peer-reviewed journals, including the Journal of the American Medical Association (JAMA).

Avoid Complacency

Never complacent, next, he focused on mental health, studying psychiatry at the Albert Einstein College of Medicine and becoming a Chief Resident and faculty member. He later committed himself to public health service as the director of community mental health centers in Westchester County, New York. He was a distinguished Life Fellow of the American Psychiatric Association.

Two medical careers – let alone one – would be enough for most. Still, Burt began his third career–in sports psychology – when he was in his 60s – incorporating a solid theoretical foundation that included a variety of psychological approaches, such as understanding the unconscious mind, focusing on the individual’s unique experiences and potential for growth, exploring the meaning of life and human existence, studying how thoughts and behaviors influence each other and emphasizing the importance of the whole experience into his work. For decades, he helped athletes identify and understand psychological barriers that stood in the way of their performance goals. Often referred to as “Yoda,” he was tapped in his 80s to become the Association for Applied Sport Psychology president.

His capacity to retain and apply information was superhuman – uncanny (and sometimes uncomfortable).  His pattern recognition skills were razor-sharp, and he operated at total capacity well into his 90s.  Before the term “artificial intelligence” was coined in 1952, Dr. Burt Giges exhibited human-derived machine learning “computing” power – pure applied intellect.  His quest for truth and optimizing human potential led him to cognitive psychology at its earliest stage.

Stretch Yourself to the Limit

“The big challenge is to become all that you have the possibility of becoming. You cannot believe what it does to the human spirit to maximize your human potential and stretch yourself to the limit.” These words reflect Burt’s belief in the transformative power of striving to reach one’s full potential. He understood that cognitive awareness could unleash someone’s potential and applied it to his progressive growth and all that sought his counsel.

Though gone, his ideas are still taught and his words continue to be studied. His call to look beyond the superficial and at the root cause of behaviors made him an extraordinary therapist. This persistence in seeking the root cause of symptoms, whether tackling mysterious infectious diseases, emotional struggles, or human dynamic challenges, made him one of a kind. This capacity made him an exceptional healer of people’s physical and mental health, enabling them to reach greater potential.  Imagine if Bert and people like him were united to define the benefits of today’s intelligent technologies. Cognitive performance opens the door to our best leveraging these expansive information processing tools.

Enter the Cognitive Age

Recently, after a conversation with John Nosta, who now appears in his third appearance on Health Unabashed Healthcare Now Radio, I wondered what would happen if Burt and John shared the same main stage. John has become the singular voice exploring the nexus between human capacity and technology as an ally. Like my father-in-law, his journey is a book of diverse career chapters. Earlier, John pursued and published cardiovascular research. He then moved into major roles at creative agencies as Chief Creative Officer, Chief Strategic Officer, and Unit President. Now, Nosta’s voice champions intellectual curiosity, disruptive thinking, and innovative problem-solving approaches. 

It would been electric to see my father-in-law Burt and John in conversation – perhaps the exchange would have been a memorable lightning round knock-out debate or two compelling people pushing us higher and higher. Two people in search of “truth.”  While I am sometimes occasionally positioned against John as “titans’ battling in the innovation ring to draw in webinar and podcast listeners, our sparring is more of a “lovefest for supporting humanity’s greater potential rather than titans in opposition. 

John – always a step ahead of the crowd – speaks to how this era should be termed the Fifth Industrial Revolution.  He refers to the symbiosis of human intelligence and computational prowess. The Cognitive Age Nosta outlines leverages AI as a cognitive partner, augmenting and enhancing human intellect. John did not know my father-in-law, and had they met and exchanged insights, they undoubtedly would have engaged in a very spirited conversation. 

Would they have agreed that AI, ChatGPT, GenAI and LLMs do not make human intelligence obsolete but amplify its potential? I consider that possibility. But more importantly, I will consider how human potential is still vastly underutilized. Technology is a threat only if we seek to shut it out and down. Guardrails are needed—not walls.

My father-in-law’s expansive knowledge of physiology and psychology —the link between physical and mental health and his desire to sharpen people’s life performance —and John’s exploration into science and creativity and how they illuminate the pathway to heightened performance – are superpowers.  Though Burt has left this world and John is very present, their approaches to unlocking human potential are essential for medical practice to advance.

AI will assist in complex problem-solving, provide insights from extensive datasets, and foster innovative approaches to age-old challenges. However, without the time to explore patient needs and look beyond the obvious, health teams will be pressured to examine the superficial presentation of disease. Why? Because medicine stands on the foundation of science but is realized through human cognitive abilities.

Reflects Nosta: “The Cognitive Age is not just a technological revolution; it is an invitation to a deeper, more meaningful engagement with our world and ourselves. The journey calls for a unique and evolving synergy between human creativity and machine intelligence. This partnership promises to redefine our cognitive abilities and expand the horizons of our achievements.”

Look Beyond the Obvious

Medicine is becoming increasingly specialized, and the cannon on knowledge has increased beyond human capabilities to remember essential data – patient history, published data, clinical guidelines, symptoms, and signs. Imagine a physician being able to listen patiently to the patient’s journey and concerns – we must come to grips with the (depressing) reality that even the most brilliant and caring physicians no longer have the time or bandwidth (or payers’ greenlight) to diagnose complex cases. Innovative technologies must augment their passion and wisdom to heal – they do not replace. Otherwise, those patients will fall between the EMR cracks.

These technologies must now become part of the physician’s black bag, and medical schools and health institutions must welcome Nosta’s “Cognitive Age” into their classrooms and clinics, combined with Burt’s incredible thirst for knowledge and honed clinical skill. Health professional staff must augment their organic intelligence in order to sharpen their clinical and cognitive saws. They must be trained to ask savvy questions and avoid knee-jerk diagnoses that may lead treatment astray. They must embrace the Cognitive Age.

My father-in-law offered some advice for the courageous entering this uncharted territory:

“I like beginnings because if you can see yourself taking a step in the direction of change long before you reach the goal, it helps you understand that you know how to change. With the knowledge that you know how to change, you’ll be encouraged to take another step and another step. Then, eventually, you reach your goal.”

Never fear change. Reinvent yourself.

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Medika Life will be on-hand and covering this gathering – meeting with attendees and sharing highlights from the conversations. Hear from Amir why this gathering – with more than 100 speakers – should be on your calendar of 2024 meetings to attend.

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