Physician and enterprise product leader. Author of the Agentic AI for Product Leaders series. Writes on AI, healthcare, and what changes when software starts acting. 15+ years at SAP, Walmart, and regulated health tech.
Asimov gave robots three non-negotiable laws. Medicine gives physicians an oath. Healthcare AI has governance, but no runtime constitution. Until safety principles are enforced at the moment of output, not just in policy documents, we are deploying systems without the equivalent of “do no harm.”
A Nature Medicine study found ChatGPT Health under-triaged 52% of real emergencies. The deeper issue may not be the model, but its training data: AI learns from documented hospital records, yet it is deployed at first contact, where the most critical signals were never captured.
The healthcare data integration problem is 20 years old and not going away. So why are we still building AI that assumes clean data? A case for designing AI that works in the real world, not the one we keep promising to build.
The New York Times asked eight leading thinkers where A.I. is headed. Most rated its near-term medical impact as small or moderate. They were looking at the wrong scale. The revolution in healthcare A.I. is already here; it just looks like a discharge summary generated in seconds, not hours.
The most important AI benchmark you have never heard of measures one thing: how long can an AI system work on a task before it loses the thread? In healthcare, that matters more than single-answer accuracy. A sepsis protocol is a multi-hour trajectory, not a prompt. Attention span is the real gate.
Healthcare AI fails not from lack of data, but from fragmentation. Each system sees a piece of data, no one sees the patient. Physicians reason in connected patterns, "Knowledge Graphs" formalize that reasoning, creating a unified, governed layer that lets AI see meaning, not noise.
Governance by design is essential, until 3 a.m. In an emergency, a perfectly compliant blood bank workflow nearly cost a life. Healthcare AI doesn’t need less governance. It needs adaptive governance, rules that scale with risk and allow accountable overrides when seconds matter.
AI governance isn’t nested boxes or monthly committees. It’s architecture. Under HIPAA and GDPR, your “data steward” often can’t even review the data. Real governance is built into the plumbing, validation, lineage, and security enforced automatically. Otherwise, it’s theater.
Healthcare is rapidly adopting Customer Data Platforms to unify patient data and personalize engagement. Used as infrastructure, CDPs improve identity resolution, consent management, and outreach. But without clear governance, they risk optimizing for engagement over clinical need.
I’m seeing a flood of self-proclaimed “AI experts” with little real depth. After decades building enterprise systems, I still hesitate to claim the title. In healthcare AI, experience matters more than hype. Projects fail from old debts and governance gaps, not lack of buzzwords.
Digital twins are moving from factories to hospitals. We’re building replicas of patients, modeling disease in real time, and of physicians, capturing decades of expertise. The twin extends capacity while preserving oversight. What will happen when they no longer need us in between?
The narrative says AI pilots fail in healthcare because the industry is slow and resistant. It is backwards. No industry changes faster than healthcare when the change actually works. The problem is not resistance. It is that most AI pilots have not earned the right to change it.