Health care is not short on ideas. It is not short on innovation, intelligence, technology or ambition. What it risks losing is focus on why those ideas matter and who they are meant to serve.

That concern shaped a fireside conversation with Tom Lawry at the Digital Health & AI Innovation (DHAI) Summit. Tom and I came to the stage from parallel and connected bodies of work. He is a best-selling author, the author of Health Care Nation: The Future Is Calling and It’s Better Than You Think, Hacking Healthcare and his classic, AI in Health: A Leader’s Guide to Winning in the New Age of Intelligent Health Systems.

Tom is one of the most respected voices on artificial intelligence and health information. My own book, Healing the Sick Care System: Why People Matter, another bestseller, looks at health care through the lives of patients, families and clinicians navigating a system that can be brilliant in moments and bewildering in motion.

Two Books, One Shared Concern

Our books were already in conversation before we arrived at the Summit. Tom wrote the Foreword to Healing the Sick Care System: Why People Matter, and I wrote the back-of-book review for Health Care Nation. We had each recognized the connection between the two. Readers, however, often encounter books separately. One may see Tom’s as a system-level call to redesign health care and mine as a people-first call to restore humanity to care. On stage, with an audience ready for the discussion, the relationship became easier to feel and internalize.

One book shares why the system must change. The other asks who that change must serve. Together, they return health innovation to the question that should guide every decision: whose life is made better because we invent?

That question was at the heart of Amir Lahav’s DHAI Summit. Curated by Amir Lahav, PhD, the Summit brings together people across artificial intelligence, digital health, health systems, research, investment and care delivery, from industry leaders such as Amazon Web Services, represented at the Summit by its Global Chief Medical Officer, Rowland Illing, MD, to trade groups like the Decentralized Trials Research Alliance championed by Craig Lipset, co-chair, and companies such as MassiveBio, an AI-powered platform to match patients to 19,000+ oncology and hematology trials worldwide, represented by Arturo Loaiza Bonilla, MD MSEd, are harnessing information to advance science and save lives.

Lahav’s achievement is far more than assembling experts. He has created a setting where different parts of the health community can bench-test their thinking against one another.  The action on the mainstage spills over to the hallways and receptions. That matters because AI in health cannot mature within a single discipline. Data scientists need clinicians. Clinicians need workflow support. Innovators need patient insight. Investors need to understand adoption. Health system leaders need to know when technology solves a problem and when it adds another layer of friction to an already complex ecosystem.

The audience brought energy to the room because the topic was more than technical. People wanted to talk about what AI makes possible. They wanted to talk about what health care cannot afford to forget. Health has become transactional. The operational aspects of care carry more friction than compassion. Patients are asked to coordinate their care across portals, referrals, insurance prior authorizations, clinical handoffs, and delayed communication. Clinicians are asked to heal while absorbing new layers of documentation, digital alerts, measurement and workflow pressure.

That is the context in which AI enters health care.

AI is curated knowledge and amplified pattern recognition. It can search for information no person could hold alone. It can surface signals, compare data, support decisions and make complexity more manageable. Used well, it can help clinicians, researchers, health systems and patients see what might otherwise remain hidden.

Harvey Castro, MD, MBA, a physician futurist and AI health-care innovator, understood that connection. He shared that he was heading to Portugal to speak on AI and health care, and that he would be reading Healing the Sick Care System during this flight. His encouragement reflected what made the Summit meaningful. The conversation was not ending with our fireside chat. It was traveling with people who are carrying the future of health AI into new rooms, new audiences and new decisions.

Insight, however, is different from wisdom. A pattern is different from a person. A recommendation is different from a relationship. AI can help reveal possibilities. People must decide how those possibilities impact the realities of illness, fear, family, access, culture and care.

That is where Tom’s work and mine meet. Health Care Nation asks why a country with extraordinary science, clinical talent, and technology continues to struggle with fragmentation, costs, incentives, and uneven access. Tom challenges the habit of waiting for someone else to fix what is broken. Policymakers, executives, payers, providers, employers, innovators and citizens all shape health care through choices, incentives, habits and expectations.

Healing the Sick Care System: Why People Matter starts from the same concern through the experience of a person seeking and delivering care. It asks what happens when a system with remarkable capabilities becomes so difficult to navigate that professional burnout leads to abdication, shifting more of the confusion, delay and uncertainty onto the very people seeking care. It looks at what care feels like when people seek treatment yet still feel lost, when they meet skilled professionals yet leave without understanding the next step, and when they are surrounded by technology yet feel lost and alone.

When Innovation Forgets the Person

Health care does not lack brilliance. It has extraordinary science, dedicated professionals, ambitious innovators and vast resources. Yet brilliance loses force and investment loses meaning when the system becomes more focused on transactions than on the people seeking care.

Soner Haci, CEO of PONS, captured that spirit after the session, writing that the story Tom and I shared was exactly why PONS was founded. His response mattered because it connected the fireside conversation to entrepreneurial purpose. Strong health companies often begin with the recognition that a problem people have learned to work around should no longer continue.

That is also why Lahav’s careful curation mattered. The Summit gave innovators a place to discuss more than what can be built. It invited people to consider whether what is being built is useful, human and ready for the realities of care. In health, possibility is never enough. The measure is whether the possibility improves the experience of the person seeking care and the person trying to provide it.

Tom is especially conscious of how many health professionals experience new technology. AI may be introduced as an aid, yet it can feel like another responsibility added to an already strained workflow. When a tool requires more clicks, more documentation, more review or more mental switching, it becomes one more demand on the people it was meant to support.

That concern should be central to the AI conversation. Implementation matters as much as innovation. AI earns trust when it reduces burden, fits the rhythm of care and gives clinicians back time for judgment, conversation and healing. A tool that adds work, noise or uncertainty to care has missed the purpose of health innovation.

Leanne West, innovation catalyst, patient advocate, connector, Chief Engineer of Pediatric Technology at Georgia Tech, and President of the International Children’s Advisory Network, reflected on LinkedIn that the fireside discussion was “speaking my language.” She highlighted a line from Healing the Sick Care System, that doctors should be people first and doctors second. Her reaction captured why the discussion resonated. The audience heard an AI conversation that kept returning to people.

That return to people is not sentimental. It is central to the challenge. People navigating illness often understand system failure with painful precision. They know where the instructions were confusing, where the portal failed, where follow-up disappeared, where a handoff became a gap and where no one seemed accountable for the whole experience.

Communication belongs in the same conversation. In health care, silence changes the experience. Confusing instructions, disconnected portals, delayed follow-up, fragmented records and unanswered questions become part of how people remember care. AI and digital health can help by making communication more useful, timely, and understandable. The goal is better understanding, not more automated volume.

Prevention also belongs in the same conversation. Health Care Nation argues that the health of people and the nation are inseparable. A country cannot continue spending enormous resources on illness while underinvesting in what helps people stay well. Healing the Sick Care System reaches that point through the patient’s experience. People should be seen, supported and guided before their physical and mental health reaches the snapping point.

This is the power of DHAI. Amir Lahav created a space where AI was discussed in the context of health’s larger obligation. Lahav even hosted a panel on pediatrics, where adults and children as young as six sat together on the mainstage, offering counsel. The conversation was not limited to algorithms, platforms or market opportunity. It asked whether innovation can reduce friction, protect health professionals, support patients, strengthen communication and make care more human.

Those are the questions that move AI from novelty to value. Can it help identify risk earlier? Can it make information easier to understand? Can it reduce administrative burden? Can it help match people to appropriate care? Can it support better conversations? Can it give clinicians back time to listen, think and guide? Can it help people feel less alone, less confused and more supported?

Together, Tom’s book and mine point toward priorities that health leaders should keep close: build around people, invest in prevention, reduce friction, protect clinicians, align incentives, listen to patients, measure outcomes and use technology wisely.

AI as Insight, Not Replacement

AI will not repair a fragmented system on its own. If incentives remain misaligned, AI may optimize the wrong outcomes. If patients remain peripheral, AI may scale impersonal care. If communication remains broken, AI may create more messages without creating more meaning. If trust is treated as an assumption, people will resist new tools for understandable reasons. This is why people absolutely matter.

The future worth building is hopeful. AI can help us see patterns earlier, connect knowledge faster and support better decisions. It can help researchers, clinicians and health systems work with greater insight. It can help people move through care with less confusion and more support. Its value grows when insight is joined with human judgment.

That was the heart of our fireside conversation, and that was why the audience response was powerful. We are not lacking ideas. We are not lacking innovation. We risk allowing health care to become ever more transactional at the very moment when technology should help us make it more connected, understandable and humane.

In Health Care Nation, Tom Lawry reminds us that we must stop waiting for someone else to fix the system. Healing the Sick Care System reminds us that every improvement must be judged by the lives of the people seeking care and the people providing it. These are companion calls to action.

“The future is calling,” as Tom writes. It may indeed be better than we think. It will become better when insight is joined with empathy, when innovation is guided by purpose and when the people touched by health-care systems shape what comes next.

AI can help us see more. People must decide what to do with what they see.

The next chapter belongs to us.

The post Health AI Faces a Human Test appeared first on Medika Life.

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.