The library
The AI in healthcare library
Everything AIMOCS publishes, open to read. Guides, a glossary, statistics trackers and the briefing archive.
Start here
The clinical AI evaluation kit
The questions to ask a vendor, a checklist reconciling the published evaluation frameworks, the red flags in a reported performance figure, and a governance committee charter with an intake form.
What this is
How the library is written
Every piece carries the same three things: numbered primary sources you can open yourself, the date it was last updated, and the name of the person who reviewed it where a person did. Nothing is bylined to an invented author.
Where the evidence is thin, the piece says so instead of rounding up, and where a figure has been superseded the tracker is corrected in the open with the date on it. The editorial policy sets out how sourcing, review and corrections are handled.
Guides
Guides: evaluating, deploying and governing
The long pieces. How to read a validation study that was written to be persuasive, what the FDA has actually cleared and what a Predetermined Change Control Plan lets a vendor change afterwards, how ambient documentation behaves once it is in a real clinic, and what a governance committee has to decide that no regulator decides for it. Six collections, each with its own index.
Evaluation 13Ambient AI 11Agentic AI 10Regulation 16Specialties 12Comparisons 11
The best AI in healthcare communities, societies, and networks in 2026
Twelve places where the people doing AI in healthcare actually gather — CHAI, Health AI Partnership, AMIA, SIIM, AIME, DiMe, AAIH, the FHIR chat, Medblocks, Health Tech Nerds, Out-Of-Pocket, and AIMOCS — compared on who they serve, what membership really looks like, and every published fee, each figure from the organization's own pages. As of 12 August 2026.
How to get into AI in healthcare: a clinician's guide
The realistic map for a physician, nurse leader, or researcher entering the field in 2026: five destinations, what each actually requires, where formal credentials matter and where they are myth, a self-directed 90-day reading path, and when a community shortens all of it. Every number sourced and dated. As of 12 August 2026.
The best AI in healthcare courses in 2026, compared
Johns Hopkins, Harvard, Stanford, MIT Sloan, and AIMOCS, weighed on what each programme publishes about itself: how current the syllabus is, how deep the generative and agentic coverage goes, what you build, what continues after the certificate, and who carries accredited credit. Every cell sourced and dated. As of 1 August 2026.
The FDA AI-enabled device list: a statistics tracker
A dated read of the FDA's AI-Enabled Medical Device List — how many devices carry an authorization, which specialties dominate, which pathways they take, and how thinly they report performance and demographics — each figure tied to the FDA or a peer-reviewed census. As of September 2026.
AI in cardiology: what is cleared and evidenced in 2026
Cardiology has the field's rare thing — a randomized trial where an AI alert changed diagnoses in routine care — alongside the largest consumer screening study ever run. A dated read of what is authorized and what the strongest trials measured, each figure tied to a primary source. As of July 2026.
AI in Emergency Care
A guide to artificial intelligence in the emergency department — sepsis and deterioration alerts, triage, and imaging detection for stroke and fracture — reading each result for the one thing that decides its value: whether it changed the workflow and the human response, rather than the model's accuracy alone. Each figure tied to its primary source. As of July 2026.
Glossary
Glossary: the vocabulary, defined once
Terms defined the way a clinician needs them defined: what the word means, what it is routinely used to mean instead, and where the definition comes from. Short entries, each with its sources. These are the pages to send to a colleague who is about to sit in a vendor meeting.
Automation bias
What automation bias is, how large the effect has been in controlled clinical studies, why expertise and explanations do little to stop it, and what to ask before an AI tool reaches your clinicians. As of September 2026.
Context window
What a language model's context window is, why long patient records strain it, what studies show about models missing information buried in long inputs, and what to ask about how a tool handles a full chart. As of September 2026.
510(k) and De Novo
The two FDA routes that bring almost every AI medical device to market: what a 510(k) and a De Novo request each prove, what they leave unproven, and what to ask before you trust a clearance. As of September 2026.
FHIR (Fast Healthcare Interoperability Resources)
What FHIR is, why US rules made it the default way software reads a patient record, how clinical AI tools depend on it, and the integration questions to settle before a pilot. As of September 2026.
Fine-tuning
What fine-tuning a model means, how it differs from prompting and retrieval, what the clinical evidence says about when it helps, and the questions it raises for data governance and device regulation. As of September 2026.
Large language model (LLM)
What a large language model is, how pretraining and fine-tuning turn next-word prediction into a clinical writing and question-answering tool, why fluent output can still be wrong, and what to ask before one touches patient care. As of September 2026.
Statistics
Statistics: running trackers, not one-off posts
Adoption, device clearances, published evidence, funding. Each figure is normalized by what it actually counts: a number about “AI-enabled devices” and a number about “clinical AI in daily use” are not the same number. Each carries the date it was true and its primary source. Trackers are revisited on a schedule rather than left to rot.
FDA-cleared AI devices by year and specialty
What the FDA's own AI-enabled medical device list shows: the cumulative growth curve through 2025, the persistent three-quarters radiology share, and the 2024 detail on specialties and transparency reporting. As of September 2026.
AI in healthcare statistics (2026)
A primary-sourced dashboard of where AI in healthcare actually stands — adoption, clinical evidence, regulation, and investment — with every figure dated and normalized by what it really counts. The hub for our statistics cluster. As of August 2026.
LLM medical benchmark results tracker
A running read of how large language models score on the leading clinical benchmarks — MedHELM, HealthBench, and the new HealthBench Professional — set beside the peer-reviewed critique of what those scores do, and do not, tell you about bedside performance. As of August 2026.
Healthcare AI funding and M&A tracker
What the three primary trackers — Rock Health, CB Insights, and Silicon Valley Bank — actually report about digital-health and healthcare-AI funding across 2024–2026, why their credible totals differ by 3x, and the disclosed deals behind the headlines. As of July 2026.
Physician attitudes to AI: the survey tracker
Three waves of the AMA's Augmented Intelligence survey — 2023, 2024, and 2026 — placed on one time series, re-read with a consistent denominator, and cross-checked against an independent physician survey. Use, enthusiasm, concern, and the training gap, each with its date. As of July 2026.
Global AI in health regulation tracker
A living status table of the rules governing AI in healthcare — what is in force, what is proposed, and the exact effective dates — across the EU, the US FDA, the UK MHRA, the WHO, and other jurisdictions, each row tied to a primary regulator source. As of September 2026.
Briefing
Briefing: one development, read closely
Each issue takes one paper, dataset, or decision: what it measured, how well it measured it, and whether it should change anything. Every issue is open to read. Members also get the full read below the line.
Briefing 001 — A generative AI copilot meets a hard patient endpoint
The first issue of the AIMOCS Briefing reads one study closely: a pragmatic, cluster-randomized trial that put a generative AI clinical copilot in front of clinicians treating nearly 10,000 patients in Kenya, then measured whether the patients did better. The notes got better. Within 14 days, the patients did the same either way — and that honest null is the most useful thing.
Briefing 002 — The largest ambient-scribe study yet counts the minutes
Ambient scribes are the fastest-spreading AI purchase in care delivery, and the pitch is time. Issue 002 reads the largest multisite cohort to date — 8,581 clinicians across five US academic health systems — and finds the time is real but modest: sixteen minutes of documentation per eight scheduled patient-hours. The after-hours record work, the part the sales pitch calls "your evenings back," did not move.
Briefing 003 — When the AI is switched off: the first deskilling signal
Four Polish endoscopy centres turned on AI polyp detection at the end of 2021. Issue 003 reads the study that looked sideways at what happened next: on procedures done without the software, experienced endoscopists detected adenomas in 22.4% of colonoscopies, against 28.4% in the months before AI arrived. It is observational, and it is the first real-world, patient-relevant deskilling signal on record — which makes reading its limits as important as reading its headline.
Join
Everything here is open to read. The network is where members work through it together.
Members post a case or a question, and physicians, healthcare executives, and researchers answer. Membership also includes the member directory, the full briefing, event registration and replays, and ten self-paced courses with certificates of completion.
One email address and a six-digit code.