Clear thinking for healthcare AI in real clinical settings
A restrained, research-minded hub on healthcare AI, clinical workflow design, governance, validation, and adoption across health systems.
This site is built to cover healthcare AI with practical depth: how systems are designed, evaluated, governed, and adopted without losing sight of clinical reality.
It is not a personal services site. It exists as an authority node for healthcare AI, with clear writing on workflow integration, diagnostics, and responsible deployment.
Latest analysis
The hospital AI tools I trust are the ones built for boring accountability
A physician-executive view of why hospital AI should be judged less by flashy demos and more by regulatory pathway, workflow fit, and post-deployment monitoring. The safest tools are usually the least theatrical.
August 25, 2026 · 10 Min ReadThe AI Governance Test Every Hospital Misses on the First Try
Hospitals do not fail AI because the model is clever or clumsy. They fail it because governance arrives after procurement, after workflow design, and usually after the first patient harm scare. The real question is whether a health system can match speed with proof, oversight, and accountability.
August 21, 2026 · 8 Min ReadThe Moment the Algorithm Needed a Human
A physician-executive lens on why hospital AI should be governed like clinical infrastructure, not purchased like software. The real question is whether an AI system can survive scrutiny after the vendor demo ends.
August 18, 2026 · 9 Min ReadThe topics this site covers
Clinical workflow design
How AI fits into triage, documentation, routing, review, escalation, and other high-friction clinical workflows.
Governance, safety, and evaluation
Practical guidance on model validation, monitoring, human oversight, risk controls, and safer adoption of medical AI.
Imaging, diagnostics, and adoption
Commentary on medical imaging AI, diagnostic support, and the organizational conditions needed for successful health-system rollout.
Positioning
Credible commentary for a fast-moving field
The tone stays clear, restrained, and evidence-aware. Claims should be specific, useful, and grounded in how care is actually delivered.
The goal is to help readers understand what works, what fails, and what responsible healthcare AI adoption looks like in practice.
About the Author
Follow the broader healthcare AI conversation
Dr. Sina Bari, MD is a Stanford-trained surgeon and the VP of Healthcare & Life Sciences AI at iMerit. He writes here on the clinical-workflow and governance side of healthcare AI, separate from the long-form essays at drsinabari.com and the surgical-practice writing at sinabariplasticsurgery.com.
For speaking, collaboration, or media inquiries related to healthcare AI, use sinabari@gmail.com.
Full profile at sinabarimd.com →