Radiology AI Podcast | RSNA

Drs. Ali Tejani and Paul Yi sit down with Dr. Jesse Courtier to explore his journey from academic radiology to startup founding and venture capital. The conversation dives into innovation, AI in radiology, and how clinicians can shape the future beyond the reading room.
Radiology: Artificial Intelligence Journal

Host Satvik Tripathi interviews guest Dr. Dania Daye about how AI is reshaping radiology; from workflow integration and governance to training, value assessment, and the future role of radiologists. Together, they explore the promise of foundation models, the need for outcome‑driven evaluation, and the importance of preparing clinicians to manage AI responsibly while preserving the human elements of care.
Radiology: Artificial Intelligence Journal

Live recording of the RSNA 2025 Fireside chat hosted by Drs. Paul Yi and Ali Tejani of the Radiology: Artificial Intelligence podcast.
This AI Fireside chat was an informal discussion with some of the leaders, movers, and shakers in the field of AI in radiology. This session was recording at RSNA AI Theater for an intimate and storied time of reflection on the year's developments in AI, discussion about where the field is moving, and lively debate over controversial topics relevant to radiology, AI, and beyond.
Our esteemed panelists included:
Charles Kahn, Jr., MD, MS - Editor of Radiology: AI
Nina Kottler, MD, MS - Associate CMO, & Clinical AI VP Clinical Operations, Radiology Partners
Woojin Kim, MD - Chief Medical Officer, ACR Data Science Institute & Chief Medical Information Officer of Rad AI
Linda Moy, MD - Vice Chair of AI for the NYU Department of Radiology

Satvik Tripathi sits down with Saurabh Jha for a candid conversation on where radiology AI is delivering real value versus hype. They explore automation, workflow impact, and the hard questions shaping the future of AI in radiology.
Radiology: Artificial Intelligence

Host Ali Tejani speaks with Dr. Laurens Topff and Stephane Willaert about how to identify high-value clinical problems in radiology and translate AI tools from research prototypes into real clinical practice. Together, they discuss lessons learned from collaboration, workflow integration, and what it takes to develop AI that radiologists will actually use.
https://pubs.rsna.org/journal/ai