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
AI Agents in Medicine Need Oversight, Not Blind Trust
AI agents may become the most useful junior member of the care team, but only if medicine redesigns supervision, escalation, and accountability around probabilistic systems. The hospitals that win here will treat agents like brilliant residents with bounded privileges, not autonomous clinicians.
June 26, 2026 · 9 Min ReadThe Working Physician's Personal AI Stack
A physician-executive’s take on the minimum viable AI stack that survives a real clinic week, and on the harder skill of knowing when not to use AI at all.
June 28, 2026 · 9 Min ReadClinical AI Is a Governance Problem, Not a Chatbot Problem
A technically strong clinical AI model can still fail if no one defines ownership, workflow placement, escalation rules, and review cadence. In hospitals, the decisive question is not which model won the benchmark, but which team is accountable when the alert fires.
June 28, 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 →