Leading AI in healthcare.

Artificial intelligence needs physicians. AIMOCS is a network of physicians, healthcare executives, and researchers who evaluate, deploy, and govern it in clinical care.

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Overview

Enthusiasm for artificial intelligence in medicine runs well ahead of the evidence for it. The distance between the two is not a reason to wait. It is the work itself: someone has to evaluate these systems before hospitals depend on them.

AIMOCS exists for that work. Members study how clinical models are built, where they fail, and what it takes to run them safely on a working ward — then they compare notes.

Figure 1 — Enthusiasm and evidence, over time since the demo. The gap is where judgment is needed.

The library

104 pieces on evaluating and running clinical AI, open to anyone — every one carries numbered primary sources and the date it was last checked.

Guides 73 Glossary 20 Statistics 8 Briefing 3

Figure 2 — Reported performance, and the point where a ward actually operates.

AUROC explained for clinicians

Evaluation · 9 min

AI scribes: what the evidence actually shows

Ambient scribes reached millions of encounters before the first randomized trial reported a result. What the peer-reviewed record actually shows.

Ambient AI · 9 min

A physician's hands annotate a printed chart with a pen beside a stethoscope in warm window light.

How to read an AI validation study

Evaluation · 8 min

Figure 3 — Four cells. Only one of them wakes anyone up.

Sensitivity, specificity, and AUROC

Glossary · 4 min

Figure 4 — The demo checklist.

The clinical AI evaluation kit

Sixteen questions for the vendor demo, a reconciled evaluation checklist, and a governance charter.

Working document

Figure 5 — Performance by day. The schedule changes; the model does not.

Dataset shift and model decay

Evaluation · 11 min

The whole library

Teamwork

No single profession can put a model into clinical care alone. The physician knows the ward. The engineer knows the failure modes. The executive owns the budget and the risk. A deployment that leaves out any of the three fails in that person’s territory.

AIMOCS keeps the three in one conversation: the same case, read from three sides.

“The model is not the practice of medicine. The physician is.”

Figure 6 — Where the clinician goes. Not a phase of the project; a part of the system.

Figure 7 — Three professions, one deployment. Signals converge at the network and continue as one line.

1,000+

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. Each one lands on somebody’s ward.

Compared with certificate programs

University certificate programs in AI for healthcare are rigorous and often excellent. Harvard, Stanford, Johns Hopkins, and MIT all run programs worth completing. A network answers a different need.

	A certificate program	AIMOCS
Format	A set curriculum with lectures and a final assessment	Continuing discussion of live cases and deployments
Duration	Weeks to months, then it ends	Ongoing; members stay as long as it is useful
Taught by	University faculty	Working physicians, engineers, and executives
You leave with	A credential	A practiced habit of evaluation, and people to call

The two are complements, not rivals. A course teaches the foundations. The network is where your questions go once the systems are live. The full comparison covers each program cell by cell.

The programs the comparison covers.

Join the network

Membership is the full library and every revision of the evaluation kit, a seat at the live cohorts as they open, and the paper feed.

Above all, it is the people: physicians, healthcare executives, and researchers doing the same work, comparing notes.

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Joining takes one email address and a six-digit code.