Current and Future States of AI in Healthcare in 2026: AIMed26

"AI in healthcare is now a governance, infrastructure, and workforce triadic imperative for any healthcare board of directors and C-suite, and no longer a portfolio of technology projects with IT oversight.”
I have been asked to give several talks on AI in healthcare to various boards of directors, and here are some of my thoughts for all leaders in healthcare:
(Governance) As AI in healthcare is transitioning from a more stable (and predictable) computational intelligence for pattern recognition (in images or clinical decisions) to a more dynamic (and less predictable) agentic AI that involves AI agents for autonomous support, governance of AI in healthcare is more crucial and urgent than ever before to mitigate this nascent AI-related risk. While the board of directors is accustomed to overseeing clinical quality and financial risk, it is not prepared for algorithmic risk- the risk that a validated model behaves unpredictably or inequitably once deployed in a new population. AI governance will need to become a board-level imperative with an AI committee and not an IT function.
(Strategy) In addition, AI in healthcare organizations has matured and can no longer be in pilot project purgatory (pilots that cannot scale or “lots of shots on goal but no goals scored”), but should rather have an AI platform strategy coupled with a capital expenditure budget to achieve longer term dividends and an infrastructure that can encompass workflow redesign and accountability. A platform approach unlocks value on two fronts. Clinically, it enables earlier and more accurate diagnosis. Operationally, it delivers timely ROI through ambient documentation, revenue-cycle automation, scheduling optimization, prior-authorization workflows, and discharge planning.”
(Education) Finally, and most foundationally, there is an escalating deficit of education and experience of AI in healthcare from the board of directors and C-suite executives to the health system clinicians and associates with an overall lack of sense of urgency for a robust AI in healthcare education program. Boards and executives who do not understand AI cannot govern it and cannot allocate capital towards it so education is the foundation for the aforementioned governance and strategy. One key objective is to achieve organizational AI fluency as the bottleneck of AI in healthcare is more often organizational learning than technology procurement.
AI in healthcare and key lessons and takeaways for any healthcare leader will often be topics of discussions throughout the AIMed26 meeting this coming November 11-13 at the sublime Renaissance Resort at SeaWorld in Orlando, Florida. A new feature this year will be AIMedX, an immersive experience of AI in healthcare. See you there!