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
What the Harvard Stanford Clinical AI Study Gets Right About Deployment
My reading of the Harvard Stanford State of Clinical AI study is that clinical AI has moved past demo theater and into a harder question: who is accountable when a model meets real workflow, real patients, and real operational constraints? I think the study matters most as a governance document, not a scoreboard.
July 30, 2026 · 9 Min ReadHealth Singularity, Medical School, and Why I’d Still Send My Kids
I used to think the safest bet for a bright kid was to hedge away from medicine if AI kept accelerating. Now I think the opposite is closer to true, because medicine trains durable reasoning, judgment, endurance, and the discipline to work with uncertainty, while the next generation of doctors will also need to be competent AI supervisors.
July 27, 2026 · 9 Min ReadThe quiet split in FDA AI approvals: why classical models keep winning while generative AI stays out of cleared medical devices
FDA-cleared AI in medicine is still dominated by classical machine learning, especially in radiology, while generative AI remains largely outside the cleared clinical device ecosystem. That gap says a lot about validation, risk, and what hospitals can safely deploy in 2026.
July 22, 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 →