AI MD: Where AI Meets Healthcare IRL

Medicine is changing fast—but it’s not always clear what actually matters.In this episode, I sit down with Dr. William Oh, Director of Precision Medicine at Yale Cancer Center and a nationally recognized leader in prostate cancer and clinical innovation, to cut through the noise and talk about where oncology is really headed. We get into AI, precision medicine, and the reality of implementing innovation in systems that don’t move as fast as the science. What’s real, what’s hype—and how do we think clearly in the middle of it?This is a conversation about staying grounded, leading through uncertainty, and building a career that evolves with medicine—not behind it.

In Episode 2, we sit down with Alejandro Berlin, MD, internationally recognized radiation oncologist and physician-leader at the University of Toronto, to explore how AI is reshaping the actual delivery of radiation oncology care. Building on his frontline experience integrating AI tools into contouring, planning, triage, and clinical workflow optimization, Ale walks us through what it really looks like to use AI in clinic today—not in theory, but in the chaos of real patients, real decisions, and real time pressures.We dig into where AI meaningfully improves quality, safety, and efficiency, where it still falls short, and how clinicians can stay empowered as these tools become routine. From strengthening QA pipelines to enabling new models of team-based care, Ale offers a candid, future-facing look at how AI can transform the clinic—and what it will take to make sure it elevates, rather than erodes, the human side of medicine.

In our debut episode, we sit down with Glenn Cohen, JD, Harvard Law professor and world expert in bioethics and health law, to discuss his provocative New England Journal of Medicine paper, “Medical AI and Clinician Surveillance — The Risk of Becoming Quantified Workers.” We unpack how AI tools could transform—not just patient care—but the way clinicians themselves are monitored, measured, and managed. From autonomy and accountability to bias and burnout, we explore what happens when medicine becomes datafied.