Analysis
2026-06-13
Medical AI should be stable enough to be safe and flexible enough to survive model drift, new evidence, and changing workflows. The right answer is not rigidity for its own sake, but a governed architecture that separates clinical intent, data plumbing, model logic, and deployment rules.
Read article →2026-06-11
I used to think the only real question in autonomous robotic surgery was when the robot would be “good enough.” Then I started looking at embodied AI outside the operating room, and the harder question became whether surgical autonomy can be certified, measured, and safely bounded at all.
Read article →2026-06-05
AI systems do not emerge from abstraction alone. They are built on invisible labor, and in medicine I think that fact changes how we should govern them, buy them, and trust them.
Read article →2026-06-01
AI systems do not emerge from abstraction, they are built on human labor that is often invisible, underpaid, and medically consequential. A physician-executive lens shows why hospitals and vendors must treat data labor supply chains as a governance issue, not a back-office detail.
Read article →2026-05-28
Ambient AI scribes do not fail because documentation automation is impossible. They fail when hospitals treat them like a transcription purchase instead of a clinical workflow redesign with governance, fallback paths, and measurable supervision.
Read article →2026-05-22
The emerging problem is not that clinicians lack AI, but that AI now lets them run too many high-value workstreams at once. In healthcare, that creates a new kind of cognitive overload, with implications for safety, attention, and decision quality.
Read article →2026-04-21
Medical technology often feels clumsy because it is built around regulatory survival, fragmented legacy workflows, and limited clinical input rather than daily use. The fix is not prettier screens alone, but physician-led design, real-world testing, and vendor teams that use their own products in the environments where care actually happens.
Read article →2026-04-20
Cognitive apprenticeship is not a nostalgic education theory, it is a practical blueprint for training both residents and AI systems to act safely under supervision. In healthcare, the same scaffolding that turns interns into clinicians may be the most useful model for aligning AI with physician judgment, workflow, and patient risk.
Read article →2026-04-01
Hospital leaders should not ask whether an AI diagnostic tool is impressive; they should ask whether it is clinically validated, operationally safe, and governable inside real workflows. Trust comes from evidence on performance, bias, monitoring, integration, and accountability—not marketing claims.
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