Founder mini-course
Critical Appraisal of AI in Healthcare
Academy's first paid product comes from the already produced Block 1.1: three lessons for physicians and medical students to separate evidence, metrics, validation, and hype in healthcare AI tools.
Curriculum
From vocabulary to clinical judgment
The mini-course is smaller than the master track and clearer as the first promise: a bounded sequence with a practical framework for evaluating healthcare AI.
01
What is, and what is not, AI in healthcare
Minimum vocabulary so clinicians can separate automation, machine learning, LLMs, marketing, and clinical evidence.
02
Anatomy of a clinical AI study
A six-question checklist to read studies, interpret metrics, and see when technical performance does not become clinical benefit.
03
Red flags and emblematic cases
A traffic-light framework to classify healthcare AI tools and claims before recommending, buying, or implementing them.
Materials
Initial block library
Critical reading
Students leave with the language and questions to evaluate AI claims without relying on external authority.
Prudent decision-making
The goal is to protect clinical practice: adopt with evidence, test with method, and reject unsupported hype.
Opening
Founder cohort through Dev Pass
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