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In their seventh Reflections episode, and their fifty-first overall, Steve and Leon look back across six conversations: Mika Newton on interoperability that finally started working, Peter Embi on monitoring clinical AI after deployment, Vimla Patel on how clinicians actually reason, Renee Deehan on an engine built so it can't hallucinate, Christine Dymek on AI literacy, and Jeremy Harper on an adoption curve that broke. They decide against forcing a single grand lesson and find three threads anyway: keeping a human in the loop as a deliberate design decision, the national clearinghouse for AI errors that still doesn't exist, and whether literacy is even the right word for what healthcare workers need.
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RECHERCHER LES ÉPISODES PASSÉS
Rechercher les épisodes précédents de Practical AI in Healthcare.
AUTRES ÉPISODES DANS CE PODCAST
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 …
The internet is full of wellness advice built on a single cherry-picked study. Renee Deehan, a molecular and cell biologist who leads science and AI at InsideTracker, spent two decades building the opposite. In this episode, she explains why the core of their recommendation engine is symbolic AI, k…
Chris Dymek came to healthcare informatics by way of philosophy, and she still thinks like a philosopher: the first question is what we actually mean. As Director of Digital Healthcare Research at AHRQ, she funded AI research, helped launch work on AI and patient safety, and wrote a request for inf…
Peter Embi has spent his career at the intersection of medicine and informatics: coining the term "algorithmovigilance," serving as the nation's first Chief Research Information Officer, and leading AI work at Vanderbilt. He is also a patient. A rare adrenal tumor took nearly 15 years and…
Dr. Vimla Patel has spent four decades studying how physicians actually reason, and what happens when technology ignores it. In this episode, she explains the difference between forward reasoning (the fast, pattern-driven hallmark of expertise) and backward reasoning (the slower, hypothesis-testing…
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