For physicians, healthcare executives, and researchers
Clinical AI needs physicians in the decision.
Courses, a sourced library, and live sessions on AI in healthcare, plus a network of the people deploying it alongside you.
Overview
AI in medicine needs physicians. Enthusiasm for these tools runs well ahead of the evidence for them, and someone has to judge each one before a hospital depends on it. That judgment belongs with the people who know the patient.
AIMOCS helps physicians judge clinical AI on the evidence, and work it out with the healthcare executives and researchers they deploy it with.
AI-enabled medical devices authorized by the U.S. Food and Drug Administration.
Source: a peer-reviewed taxonomy across 1,016 authorizations (Singh et al., npj Digital Medicine, 2025), read against the FDA’s own device list in our tracker.
What is here
Courses
Ten self-paced courses in four tracks. Every one of the 129 lessons ends in a hands-on exercise.
Build tracks are for people who write the code, Lead tracks for the people who direct them. Programming Foundations and AI in Healthcare Operations each count in two tracks.
The library
112 pieces on evaluating and running clinical AI, each with its sources and the date it was last updated.
Guides 72Glossary 29Statistics 8Briefing 3
Live
A cohort that works through the material together, and events with the people building AI in healthcare.
Teamwork
No profession puts clinical AI into care alone. The physician knows the patient and the ward, the researcher knows where the model fails, and the executive owns the budget and the risk. Leave one out and the deployment fails where that person would have caught it.
The AIMOCS network is where physicians, healthcare executives, and researchers work it out together.
The model is not the practice of medicine. The physician is.
What members do
- Post a case or a question in the community, reply, and react.
- Browse the member directory, with each member's role, institution, and country.
- Read the full briefing, including the part below the line.
- Register for live events and watch the replays.
- Take ten self-paced courses, 129 lessons in all, and earn a certificate of completion for each course you finish.
After the certificate
Harvard, Stanford, and Johns Hopkins run rigorous, often excellent programs in AI for healthcare. A certificate teaches the foundations and ends. The network is where the work continues, with the people deploying the same tools.
AIMOCS and three university programs, row by row. Sources follow the table.
| Feature | AIMOCS |  Johns Hopkins |  Harvard |  Stanford |
|---|---|---|---|---|
| What continues after | The member community, live events and replays, and the research briefing | Program ends at certificate [1] | Program ends at certificate; alumni events vary [2] | Program ends at certificate [3] |
| What you build | A hands-on exercise in every one of 129 lessons, across ten courses | Guided projects + masterclass build [1] | Demonstrations only [2] | Recorded exercises + capstone [3] |
| Generative + agentic AI | Core to two of four tracks: LLMs end to end, agentic AI engineering, scribes, golden-set evals, MCP, EHR agents, and governance. A separate course covers working with Claude | Curriculum runs from LLMs to goal-directed agentic AI, plus an n8n masterclass [1] | Curriculum covers generative AI; hands-on sessions are demonstrations [2] | No agentic-AI module in the published syllabus; core CME content released Aug 2023 [3][3a] |
- [1] Johns Hopkins: AI in Healthcare Certificate Program
- [2] Harvard Medical School: “AI in Clinical Medicine”
- [3] Stanford: AI in Healthcare Specialization (Coursera)
- [3a] Stanford: Fundamental Machine Learning for Healthcare (CME first released 10 Aug 2023; renewed through 9 Aug 2029, as rechecked 9 Sep 2026)
Bring your judgment to the network.
Post a case, take a course, or join a live session with the physicians, healthcare executives, and researchers deploying the same tools.