Explore every episode of the podcast The Brain and the Bot Podcast
| Title | Pub. Date | Duration | |
|---|---|---|---|
| #4 Learning, in the Mind of the Beholder | 06 oct. 2026 | 00:33:15 | |
At end-of-week feedback, two learners said the same thing: good week, but "I only saw one or two disease states — I wish I'd seen more." That comment kicks off this episode. Is the scavenger-hunt view of a clinical rotation the right one, and what does it miss when the pneumonia patient in bed four is there because of a catastrophic stroke two months ago? We work through what competency-based medical education has actually taught us after a dozen years: that a checklist of observed EPAs can't capture competence, because competence is the ability to adapt to a shifting mix of contextual variables — the wheezing patient with no edema and no history, who doesn't speak your language. Then we borrow two ideas from machine learning to reframe it. The long tail: you can build a system (or a learner) that handles the textbook 95% fairly easily; it's the endless stream of edge cases that's hard and high-stakes. And zero-shot learning: a toddler needs two picture books to tell a cow from a bird, while older algorithms needed thousands of ICU patients to recognize sepsis — so what does it take to generalize from one heart-failure patient rather than fifteen? That leads somewhere unexpected: maybe junior learners should move faster through the core, and senior learners should be allowed to slow down and dig into context? Chapters Mentioned in this episode Competency-based medical education (CBME), EPAs, and ITERs
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| #3 Should AI summarize the morning handover?? | 06 oct. 2026 | 00:35:56 | |
What Is Clinical Practice? Morning handover is the most expensive hour of the day: an exhausted post-call team presenting overnight admissions to an incoming team that hasn't started yet. It's non-negotiable, it's where a huge amount of teaching happens, and it's a prime target for AI summarization. In this episode we ask whether it should be. We start by pulling handover apart into its two jobs — the clinical transfer of care and the educational exercise of synthesizing a case and pitching it — then dig into what AI summarization actually is under the hood. Why large language models confabulate rather than "hallucinate," why probabilistic output is a different kind of computer than the deterministic calculators clinicians are used to, and what it means when a scribe tool writes a normal abdominal exam that never happened. From there: should the learner's presentation be replaced by an AI summary, or compared against one? What gets lost if trainees never learn to tell the story of a case? And borrowing from self-driving cars and aviation, what error rate is medicine actually willing to accept from a machine versus a human? Chapters Mentioned in this episode Melvin et al. (Toronto, 2019) — qualitative study of the oral case presentation in internal medicine handover
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| #1 Beyond Medical Expertise, AI Imposters | 06 oct. 2026 | 00:33:33 | |
From an AI corollary, the things that have made language models dramatically more useful over the past two years — context engineering, "soul" and user documents in agent frameworks like OpenClaw, built-in self-reflection loops — look a lot like what we're trying to instill in advanced learners: values, systems thinking, and a sense of the culture they're entering, not more facts. We also ask whether AI models suffer from imposter syndrome or overconfidence, why sycophancy is a self-efficacy problem, and what a model "breakdown" after failed attempts tells us about the cultures we build for humans and machines alike. Chapters Mentioned in this episode Northouse, Leadership: Theory and Practice (behavioral approach) AI use: | |||
| #2 Deskilling | 23 juil. 2026 | 00:34:47 | |
Looking at the topic of possible "Deskilling" when it comes to using AI tools in medicine. What is "deskilling" What's the history of "deskilling" in medicine Newest evidence of "deskilling" in medicine. | |||