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 Hospital Boards Get Wrong About AI Governance
Hospitals keep asking whether an AI tool is accurate enough, but the sharper question is whether the tool fits the regulatory pathway, the workflow, and the people who will live with its mistakes. In practice, the difference between a cleared model and a safe model is local governance, not marketing.
October 16, 2026 · 8 Min ReadEvaluation and Validation of Agentic Processes in Healthcare: What I Look for Before Anyone Calls It Safe
A physician-executive perspective on how to evaluate agentic AI in hospitals, with a practical framework for validation, governance, and failure detection before deployment. The real test is not whether the agent looks smart in a demo, but whether it survives clinical workflow, safety review, and regulatory scrutiny.
October 10, 2026 · 9 Min ReadAgentic AI in Clinical Medicine Is a Control Problem, Not a Demo Problem
Agentic AI in medicine fails or succeeds at the handoff points, where plans become orders, and orders become patient care. The right question is not whether the agent sounds smart, but whether the hospital can constrain, audit, and reverse its actions.
October 2, 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 →