Examining Open-Source Frameworks and AI in Medicine with Interventional Radiologist Dr. Judy Gichoya

Dr. Michał Nedoszytko, clinician scientist at Abridge and interventional cardiologist, has spent two decades building software alongside clinical practice. He explains how AI can help physicians gather better information before an encounter, improve the quality of conversations with patients, and prototype solutions to problems that clinicians experience every day. Nedoszytko argues that medical training gives physicians lived knowledge that they can bring to building software. Yet he draws a firm distinction between making software and making software safe. In regulated clinical environments, engineers, privacy protections, and rigorous delivery remain indispensable. As AI assumes more analytical and administrative work, he hopes medicine becomes more grounded in its human purpose: clinicians closer to patients, present in the moment, rather than focused on screens and documentation. Transcript.

The promise of medical AI is not simply better performance — it is better care grounded in evidence patients and clinicians can trust. Dr. Xiao Liu, a clinician and medical AI researcher affiliated with the University of Birmingham and Microsoft AI, has spent her career asking what rigorous evaluation should look like as new technologies move from publications into practice. She describes reporting guidelines as tools for transparency rather than prescriptions and explains why seeing product development from inside industry changed her understanding of implementation. Looking ahead, Liu imagines a medical profession in which readily available knowledge shifts physicians toward a more interpersonal role: accompanying patients through uncertainty, deterioration, treatment failure, recovery, and life-changing diagnoses. At the same time, she sees AI opening new scientific possibilities by revealing biomarkers, disease phenotypes, and treatment-response patterns that current medical categories may miss. The challenge is to preserve rigor without letting familiar methods prevent medicine from learning something genuinely new. Transcript.

A patient can deteriorate while the warning signs remain buried in a noisy electronic record. Dr. Suchi Saria, Founder and CEO of Bayesian Health and a faculty member at Johns Hopkins University, has spent her career trying to surface those signals sooner. She explains why a strong retrospective model is only the starting point — and why prospective validation, workflow integration, physician adoption, monitoring, and financial sustainability determine whether clinical AI ever reaches patients. Her work on early sepsis recognition became personal after she lost her nephew to the condition, turning an academic agenda into an urgent effort to prevent failures to rescue. Saria also challenges the current center of gravity in health care AI: tools increasingly prepare for visits, transcribe conversations, and support billing, while comparatively few directly help clinicians care for patients. She envisions a third modality of medicine alongside drugs and devices — rigorous, software-based interventions that identify when to act, what to do, and for whom. Transcript.

Brandon Rice believes that improving health care sometimes means improving the systems behind it. Through his work at Weave, he focuses on modernizing the regulatory infrastructure that helps bring new medicines to market. He discusses the challenge of organizing scientific knowledge, communicating with regulators, and navigating processes that can span more than a decade. His vision is that AI can reduce administrative burden while preserving the rigor required for patient safety and scientific progress. Transcript.

Medical expertise has always been scarce. Dr. Karan Singal believes AI can help change that. Drawing on his work at OpenAI and earlier efforts behind Med‑PaLM, he discusses how clinicians and patients are already using AI to answer questions, support decisions, and navigate care. He argues that the future of health AI is not only about improving model performance, but also about helping people advocate for themselves more effectively. Through HealthBench and ChatGPT for Clinicians, his team is exploring how to make these systems safer, more useful, and more trustworthy. The result is a vision of health care where expertise becomes more accessible without losing sight of clinical responsibility. Transcript.