A plain-English introduction to AI for clinicians — what it is, where it touches medicine, and how to use it safely and legally with patient data.
Who it's for: Practicing physicians and advanced practitioners across all specialties — no technical background required
A plain-English introduction to artificial intelligence for clinicians — what it is, where it already touches medicine, how to use it safely and legally with patient data, and how to build a personal AI workflow that saves time without creating liability.
10 lessons · $299 · lifetime access · certificate of completion
Takeaway: AI is a powerful pattern engine, not a colleague. Once you understand that it predicts rather than reasons, you can use it with confidence — and catch its mistakes.
Takeaway: Underneath the jargon, it's a simple loop: examples tune weights, layers of neurons form a network, and tokens let it work with language. Understand that, and the rest of AI stops being mysterious.
Takeaway: AI is already in medicine — mostly in documentation and the back office, not diagnosis. Knowing the map tells you where the real time savings are for your practice.
Takeaway: No PHI in a tool without a BAA. Master de-identification and the enterprise tiers, and you can use AI every day without legal exposure.
Takeaway: A rule-based system reasons but never learns: a knowledge base of human-authored rules, an inference engine that chains them. It's transparent and dependable inside its rules — and capped by everything an expert couldn't write down.
Takeaway: Machine learning doesn't follow rules — it finds them. A model is a function fit to data by minimizing a loss, one downhill step at a time, with back-propagation sharing the error across every weight. The danger is always overfitting — memorizing instead of learning — which is why you judge a model only on data it never saw, using sensitivity, specificity, and AUC rather than accuracy alone.
Takeaway: A large language model turns text into tokens and embeddings, uses attention to read each word in context, and generates an answer one token at a time — always the most plausible next word, which is not the same as the true one. That gap is why they hallucinate. Retrieval-augmented generation closes it by feeding the model real sources to answer from — which is exactly how Doximity and OpenEvidence keep their answers tethered to the evidence.
Takeaway: In the business of medicine, AI pays for itself first in the back office — prior auth, denials, coding, documentation — where the work is repetitive and the safety bar is about time, not diagnosis. Size the prize with volume × time × rate, judge tools by ROI and break-even, and never cross the autonomy line: AI drafts, a human reviews and signs. Speed is the gift; accountability stays with you.
Takeaway: A CPT checker is the whole course in one small app: a React front end and a FastAPI backend, a single Bedrock call that asks Claude to read the note and return structured JSON, and — the part that makes it responsible — hard validation against the real code set plus a human in the loop. The model proposes; deterministic rules and a clinician dispose. That skeleton — read, propose, validate, confirm — is how to build, or judge, almost any clinical AI.
Takeaway: You came in with one intimidating word and you leave able to take it apart: three engines — rules, machine learning, language models — each with a predictable strength and failure; four habits that outlast any tool; and five questions that size up anything a vendor shows you. You don't need to fear AI, and you don't need to be dazzled by it. You can reason about it — which is exactly where a physician should stand.
Finally, an easier way to understand Artificial Intelligence and how it can affect my practice. Great courses and easy to follow.
Yvonne L. Figarella, OB/Gyn