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 AI Question Hospitals Keep Asking Too Late
Last Tuesday, a clinician showed me an AI demo that looked polished until we asked a simple question: what happens when the model is wrong on a crowded ward at 2 a.m.? Hospitals do not need more AI hype, they need governance that treats deployment like a clinical intervention.
September 1, 2026 · 9 Min ReadWhen Hospitals Adopt AI, Governance Has to Work Like Clinical Safety
A physician-executive case for treating hospital AI like a safety program, not a software feature. The real question is whether governance can keep pace with model drift, workflow messiness, and clinical accountability.
August 28, 2026 · 7 Min ReadThe 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 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 →