Steve and Leon have reviewed blocks of guest episodes twice before on Practical AI in Healthcare. Both times the themes snapped into place. This time they didn't -- and the disagreement between them became the episode. Across five recent conversations, they found stories that kept spilling past the edges of their framework: AI that works but can't get paid, laws that already apply but nobody realizes it, and a scientific record under threat from AI-generated paper mills. The hosts' attempt to make sense of it all reveals where their thesis holds, where it breaks, and what needs to change.

Bob Wachter wrote the book on the EHR disaster. Now he's written one about AI.

The UCSF Chair of Medicine joins hosts Steve Labkoff and Leon Rozenblit to discuss A Giant Leap, his argument that AI doesn't need to be perfect—it needs to beat a healthcare system already failing at scale. They cover Watson's $3B collapse, why ambient scribes became AI's first clinical success story, the human-in-the-loop problem that nobody has solved, and the dangerous gap between how experts and novices use AI tools.

Key topics: productivity paradox, complementary innovations, clinical decision support design, AI literacy, and the "compare me to the alternative" thesis.

The link to the book can be found here: https://a.co/d/07JFNwIw

What if early signs of psychosis could be detected from how patients speak—not what they say, but how they organize their thoughts?

Amar Mandavia (VA Boston, Boston University) and Enrique "Kike" Gutiérrez (Polytechnic University of Madrid) join hosts Steve Labkoff and Leon Rozenblit to discuss CHiRP, an AI tool that identifies formal thought disorder from routine clinical conversations. They explain why the gold-standard manual test takes 5+ hours, how their system reduces that to minutes, and the hard ethical questions around labeling patients as "at risk."

Key topics: prodromal psychosis detection, NLP in mental health, clinical workflow integration, MIT linQ Catalyst, and the payer challenges that make prevention hard to fund.