When physician Barry Chaiken was diagnosed with prostate cancer, his clinical training gave way to fear. It took a friend asking, "What are you doing?" to snap him back into doctor-mode thinking. That experience reshaped how he sees AI in healthcare. In this episode, Chaiken draws on his dual perspective as physician and two-time cancer survivor to argue that consumer health AI is failing patients, not because the models are bad, but because patients don't know how to use them. He shares a practical framework for AI-assisted patient education, makes the case for an aviation-style safety reporting system for healthcare AI, and explains why interoperability is an incentive problem, not a technology problem.

In Part 2 of our conversation with Matt Truppo, Global Head of Research Platforms and Computational R&D at Sanofi, we move from discovery to development, where the real stakes begin. Matt unpacks the promise and limitations of “digital patient twins,” a concept often described as the holy grail of drug development. With nearly 90% of drugs failing in clinical trials, even modest gains in predicting efficacy or patient response could transform the industry. Through real-world examples, including Dupixent and rare disease therapies, Matt shows how quantitative systems pharmacology (QSP) and AI-driven simulations are already shortening timelines, reducing patient burden, and, in some cases, eliminating the need for entire trials.

But the story doesn’t stop at modeling. We explore how AI is reshaping clinical operations, from Sanofi’s “clinical control tower” that integrates trial data across 4,000 users, to generative AI tools that are cutting regulatory document creation time by more than a third. Matt also shares a personal experiment, building a network of AI agents modeled on his own workflow, reclaiming 30% of his time and offering a glimpse into a more “agentic” future of work. The throughline is clear: AI is not replacing human expertise, but amplifying it, helping the industry finally bend the cost and time curve of drug development.

Ted Shortliffe built MYCIN at Stanford in the 1970s, one of the first medical AI systems ever deployed in a clinical setting. Five decades later, he joins Steve and Leon to examine what has persisted in clinical decision support — above all, the demand for explainability — what has changed (computational power finally caught up to the ideas), and what the field may have lost along the way. The conversation includes a direct response to Bob Wachter's claim from S1E24 that AI in healthcare decision support was "too hard a problem to start with," and a case for why structured knowledge representation deserves a second look in the age of LLMs. For anyone tracing the arc of medical AI history, this episode is a rare primary source.