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Healthcare leaders tell MobiHealthNews that 2026 will be the year artificial intelligence is put to the test.
Executives across health systems, startups, big tech and investors predict a shift in AI use in healthcare that is more deeply integrated into clinical and operational workflows, governed with clearer guardrails and held accountable for delivering measurable outcomes.
Here is Part 1 of healthcare leaders’ perspectives on where AI will make an impact next year:
Sean Mehra cofounder and CEO of HealthTap
Moving into the next year, we predict AI will evolve from an administrative assistant into a partner in personalized clinical decision-making, accelerated by major regulatory shifts. The newly announced CMS ACCESS Model and FDA TEMPO Pilot will be critical drivers of AI adoption next year. The ACCESS model is fascinating because it finally unlocks value in outcomes rather than fee-for-service. This will further incentivize the industry to leverage AI not just to drive utilization (more visits), but to drive actual health outcomes in lieu of utilization.
Additionally, the new FDA pilot, TEMPO, suggests that medical software and hardware can now be much more innovative and deployed more nimbly than before. We expect this to dramatically shorten the cycle between AI development and clinical impact.
The Rise of "Agentic" AI: Just as the FDA has recently deployed "Agentic AI" for its own internal workflows (as of Dec. 1, 2025), we expect the sector to embrace autonomous AI agents. These agents won't just flag data; they will automate the crafting of personalized interventions, drafting messages or care adjustments for the physician to review. This makes care proactive and highly personalized without increasing the time burden on the physician.
Brijesh Patel, head of innovation and AI at Pyx Health
Looking to 2026, I see three shifts. First, health systems and plans will move from pilots and demos to measurable proof. The question will be, does this AI reduce documentation time, retain more members on Medicaid and keep people out of the emergency department? If you can’t tie the model to a real outcome, it won’t survive the next budget cycle.
The second shift will be from "clicks and opens" to activation. Engagement metrics will matter less than whether people actually complete cancer screenings, keep their benefits, or start using food and transportation support. AI that can discover and address specific barriers, not just identify a need and issue a referral, is what payers will pay for.
Finally, I think in 2026 we’ll see a transition from wild-west agents to scoped copilots. 2025 gave us a taste of autonomous AI agents in healthcare. The systems that stick will look more like copilots embedded in well-defined workflows, with clear guardrails and human "escape hatches" than like free-form chatbots answering any question.
2025 was about wiring AI into the plumbing of healthcare. 2026 will be about accountability – proving that those systems actually change clinician workload, member behavior and health outcomes.
Orr Inbar, CEO and cofounder of QuantHealth
In 2026, I think we’ll begin to see more agentic workflows emerge with a human-centered approach. Large language models are especially effective in areas such as regulatory submissions, medical coding and prior authorization, all of which require human expertise.
Within venture capital, Biotech VCs will begin relying on predictive software to inform their investments. With investor appetite for therapeutic investments dropping in recent years, those who remain will likely become more sophisticated and leverage highly innovative approaches typically reserved for big pharma to de-risk their investments.
Additionally, while some big pharma companies will lean primarily on in-house teams and AI platforms, the majority will seek external innovation by partnering with relatively obscure AI startups to stay ahead of the curve.
Eyal Zimlichman, founder and director of ARC and chief innovation, transformation and AI officer at Sheba Medical Center
We've seen some integration and pilots in 2025, and we'll see more integration in 2026, which will create more of an impact on a larger scale.
Video analytics is another area of AI that we have on our radar. Traditionally, AI has been used in imaging, radiology and pathology. Now with large storage and computer power, we can perform high-level analytics on video. There's a huge amount of use cases, for example: monitoring, patient deterioration, safety events, monitoring staff hand hygiene. There's video in every environment.
Eirini Schlosser, founder and CEO of Dyania Health
In 2026, I expect AI to become more collaborative rather than transactional. Instead of single-site pilots, we’ll see multi-institution learning systems where models update continuously based on shared clinical insights, new evidence and real-world populations. That shift will force the industry to take validation, governance and equity more seriously, but it’s also what will unlock meaningful access to advanced diagnostics and therapies.
Shai Policker, cofounder and managing partner at Edge Medical Ventures
We are only at the beginning of AI’s transformation of healthcare. The changes we are seeing now are more superficial, but they will go much deeper as the technology matures. The impact on diagnostics will be profound. AI will help prevent many catastrophic events that are currently considered unavoidable, even though their early signs are buried in vast amounts of data that only AI tools can detect.
I also see major AI-driven impact in areas and indications that suffer from a lack of trained personnel. Democratizing diagnostics and treatment in emerging markets, as well as in remote areas of developed countries, is a major unmet need and a significant opportunity. As global demand for healthcare grows, the challenge will be ensuring better access regardless of location, and AI can help support that.
Edmund Jackson, cofounder and CEO of UnityAI
In 2026, providers will become more discerning around AI investments. Having had a couple years to test and pilot various tools has laid bare the problems with shiny tech that's not integrated well. Companies doing the hard work of technical and workflow integration are going to scale massively next year, while those that don't are going to be stuck in small pilots and limited deployments.
Neil Patel, head of ventures at Redesign Health
AI quietly became a "team member" in numerous organizations, listening to visits and drafting notes, prepping charts, managing inboxes, suggesting codes, and supporting prior authorization and revenue cycle. This work, largely operating in the background, provided very real time savings. It also began supporting regulators and payers by speeding evidence review and policy work.
For 2026, I predict a major shift from single-point tools to orchestrated AI workflows and agents that complete multi-step tasks end-to-end, alongside more patient-facing experiences tightly integrated into care plans, not just standalone chatbots. Organizations will also demand clear ROI, safety evidence and governance before scaling, requiring more rigorous measurement.
Nitesh Shroff, cofounder and CEO of Arintra
Looking to next year, AI’s most meaningful impact won’t come from front-end tools but from modernizing the back office. Coding, claims and denials carry heavy administrative burden and financial risk, yet they are the workflows most ready for safe, scalable automation.
As health systems move from pilots to enterprise adoption, AI will improve accuracy, accelerate cash flow and create human capacity where it matters most.
In 2026, the story of AI will be simple: less administrative drag, stronger financial performance and more people doing the work only humans can do.
Emily Greenberg, cofounder and president of Joy Parenting Club
In 2026, the next leap will be context-aware care. That means systems that understand clinical data and lived experience together. The organizations that succeed will be the ones that combine intelligence with empathy and prioritize trust as much as efficiency.
Anu Sharma, founder and CEO of Millie
I expect to see an acceleration of drug discovery, more frontline clinical operations in care delivery companies (i.e., phone calls, referrals, etc.) and all things revenue cycle from billing through claims adjudication.
I anticipate 2026 to spark new kinds of questions exploring "who" is delivering care as it becomes increasingly harder to distinguish between human providers and AI agents, and whether the productivity and efficiency gains created by AI should be additionally reimbursed.
Monica Cepak, CEO of Wisp
AI made care far more efficient behind the scenes. It hasn’t replaced clinical decision-making, it’s improved it.
Looking ahead, I expect AI to make care even more proactive, especially in preventative women’s health. We’ll see smarter triage, more personalized treatment paths and better integration between virtual and in-person care.
I also see more partnerships that bring multiple treatments and specialties under one roof happening. Bringing all the pieces together under one roof is really how we’re solving for a highly fragmented landscape that I think, over time, will continue to become consolidated.
Cherry Drulis, director of healthcare mobile B2B for Samsung Electronics
In 2025, we've seen AI truly start to transform healthcare, moving from experimentation to operational reality in healthcare settings. This has been most visible in diagnostic support, improving accuracy and personalizing treatment plans and making administrative processes more efficient. More importantly, we’re seeing AI’s ability to assist with early disease detection, enabling more proactive intervention and improving patient outcomes.
Looking ahead, I expect that AI will further push the boundaries of what's possible in healthcare by enhancing telemedicine, enabling real-time health monitoring and facilitating drug discovery and development.
Roland Rott, president and CEO of imaging at GE HealthCare
Looking ahead to 2026, AI will continue to deepen its integration across healthcare, further transitioning from a reactive tool to a proactive and predictive one. We anticipate a greater focus on enterprise-wide AI orchestration, with the rise of multi-modal AI integrating imaging, laboratory and genomic data to provide a more comprehensive, holistic patient view.
AI deployment at scale is expected not just in top academic hospitals but across community settings, with the potential development and adoption of generative AI tools that may be able to help draft reports, summarize findings and automate workflows. Together, this AI development aims to support more precise diagnostic predictions, highly personalized treatment modifications and continuous patient monitoring, further solidifying AI’s role in delivering better, more accessible healthcare.
