Most clinicians now use AI. Few have been trained on how it fails. Medical AI Competence teaches the skills that close that gap — how large language models work, where they break, and how to use them safely for your patients.
AI is already writing notes, answering patient questions, and suggesting differentials. Most clinicians treat it like a search box — type a question, accept the answer. Competent use rests on two skills, in this order:
The quality of an AI answer is set before the model writes a word — by how you define the clinical question and engineer the context: the patient's situation, the decision point, the constraints, what you already know, and what you need back. This is the core craft of clinical AI use, and it occupies most of the curriculum: defining the question, choosing the right tool, framing context, and building stepwise workflows.
Even a well-framed question can return an answer that is confident, fluent, and wrong. The three failure modes that do most of the damage: fabrication — citations, doses, and studies that don't exist, formatted exactly like ones that do (you just saw it above); sycophancy — present a wrong premise with confidence and the model validates it; and stale confidence — a guideline replaced last year, summarized with today's certainty.
If the pace of AI feels impossible to keep up with, that's because it is. No clinician can track every model release, and none needs to. Feeling behind is nearly universal — including among the people who teach this. Competence is not consumption. It is a small, durable set of skills — framing the question, supplying the context, verifying the output — that holds steady while the models change underneath it. That is what this course teaches.
A sequenced curriculum — from how the technology actually works, through verification and failure recognition, to ethics, governance, and the standard of care. Each module is free, takes a few minutes, and ends in a real assessment.
Prediction, non-determinism, and why confident text is not truth.
→ 2Match AI use to the task: counseling, documentation, differentials, workflow.
→ 3Fit the model to the clinical job, privacy needs, and setting.
→ 4Check facts, references, doses, and calculations before clinical use.
→ 5Frame the patient context and decision point. Better prompts, stepwise workflows.
→ 6Detect hallucination, outdated guidance, bias, sycophancy, and overconfidence.
→ 7Let AI handle retrieval so you can focus on judgment and interpretation.
→ 8Readability, plain-language explanation, informed consent, and correcting misinformation.
→ 9Protect PHI, document responsibly, understand medicolegal risk.
→ 10Safe implementation, team oversight, and readiness for changing standards.
→Ten modules, a 10-question assessed examination, and a certificate of completion at 70% or above. Designed for physicians, residents, nurses, midwives, and advanced practice providers. No technical background assumed. Free.
Start the course See the competenciesAI is most useful to the people who understand its limits — whether they write the prescriptions or fill them at home. Choose the course built for you.
Ten modules on how large language models work, where they fail, and how to use them safely in patient care. Assessed examination and certificate of completion. Free, ~60 minutes.
Start the clinician course → For patients & the publicA plain-language companion course on using AI wisely for your own health: what to ask, what to verify, what never to type into a chatbot. Same evidence standards, built for everyone.
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Amos Grünebaum, MD, is Professor of Obstetrics & Gynecology and Maternal-Fetal Medicine at the Zucker School of Medicine at Hofstra/Northwell and Senior Ethics Consultant at Northwell Health. In more than fifty years of clinical practice he has delivered over 10,000 babies and published more than 175 peer-reviewed papers.
He was writing about large language models in clinical medicine before most of medicine had tried one — and he has spent the time since building tools, courses, and frameworks that turn AI enthusiasm into AI competence. He publishes ObGyn Intelligence at obmd.com and maintains a free suite of evidence-based clinical tools at tools.obmd.com.
Grünebaum A, Chervenak J, Pollet SL, Katz A, Chervenak FA. The exciting potential for ChatGPT in obstetrics and gynecology. Am J Obstet Gynecol. 2023;228(6):696-705. doi:10.1016/j.ajog.2023.03.009. PMID 36924907. Published online March 14, 2023.