
Every technology healthcare has adopted moved through the same curve: a bleeding-edge few went first and documented what broke, and the majority followed. Large language models skipped that entirely. Jeremy Harper, a biomedical informatician who has worked at Epic, Ohio State, and Regenstrief, and who wrote Large Language Models (LLMs) for Healthcare, explains what we gave up by going all at once. Ambient scribes are everywhere, and because most vendors discard the audio as soon as they transcribe it, nobody can say how often the notes are wrong. He offers a fix borrowed from de-identification, a one-question test for any AI vendor, and a lament for the reusable knowledge we keep paying for and deleting.
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S2E3 - Randi Seigel & Jared Augenstein: Healthcare AI Law

S2E2 - Greg Raiz: Data-Driven Early-Stage Investing

S2E1 - Raihan Faroqui, MD: The Doctor at the Front Desk, AI on the Line

S1, E52 - Reflections #8: Year in Review
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