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
Agentic 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 ReadAgentic AI in Clinical Medicine Needs a Zero Trust Mindset
Agentic AI in clinical medicine is moving fast enough to tempt hospitals into giving it broad autonomy before the governance, security, and clinical guardrails exist. The right question is not whether these systems can act, but which actions belong inside a clinician-supervised workflow and which ones should never leave the sandbox.
September 5, 2026 · 10 Min ReadWhy hospital AI needs a clinical governance committee before it needs another demo
Hospitals do not fail at AI because the models are always bad. They fail when procurement outruns governance, clinicians cannot see the basis for a recommendation, and no one owns the workflow when the tool misfires.
September 4, 2026 · 8 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 →