Radiology AI Podcast | RSNA

Dr. Dania Daye, associate professor and vice chair for practice transformation who applies machine learning to interventional radiology, discusses how AI is changing radiology practice. She covers governance and who should decide deployments. She talks about measuring real clinical value versus simple efficiency gains. She outlines monitoring for model drift, training clinicians to spot failures, and which human roles remain essential.

Linda Moy, MD, radiologist and AI governance lead at NYU; Woojin Kim, MD, MSK radiologist and AI strategy officer; Nina Kottler, MD, MS, applied-math–trained clinical AI implementer; Charles Kahn, Jr., MD, MS, radiology AI journal editor. They debate clinical deployment of foundation models, draft reporting and workflow integration, human-AI collaboration risks and safeguards, multimodality tools, adoption speed, regulation, and governance.

Saurabh Jha, associate professor and candid radiology AI commentator known as Rogue Rad, cuts through hype with blunt takes. He contrasts vendor vs homegrown tools, predicts automation wins like chest x‑ray measurements, warns about risky copilot UIs and legal traps, and highlights radiomics, scan acceleration, and equity challenges in adoption.

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

Our hosts, Ali and Paul, speak with Dr. Udunna Anazodo and Dr. Marouf Adewole about their groundbreaking work on the BRATS Africa challenge and building AI-ready brain tumor imaging datasets across Nigeria. They share insights into the challenges of medical imaging in resource-limited settings, the power of global collaboration, and how their efforts are shaping the future of inclusive AI in radiology.

In this episode of the Radiology Artificial Intelligence Podcast, host Dr. Paul Yi speaks with Drs. Eric and Kevin Wu, recent Stanford PhDs, about their journey through academia, industry, and the startup world. They dive into their latest project, MedArena, a physician-powered platform designed to evaluate medical LLMs, and explore how AI can be more effectively integrated into real-world clinical workflows.

Dive into groundbreaking research focusing on AI's role in brain MRI for Alzheimer's assessment. Discover how the NeuroHarmony model tackles scanner variability for more reliable imaging. Learn the significance of harmonization in neuroimaging and its impact on early disease detection. Explore the benefits of using multi-class models to enhance Alzheimer’s classification. Understand the advancements in integrating volumetric MRI data across different scanners, paving the way for improved evaluation tools in neurodegenerative disease assessment.

Join Dr. Tessa Cook, an Associate Professor at the Perelman School of Medicine and a leader in radiology practice transformation, as she shares her insights on mentorship in informatics. She emphasizes the importance of encouraging tech-savvy students to explore radiology careers, bridging the gap between two worlds. The dynamics of successful mentor-mentee relationships are discussed, highlighting accountability and the need for the right match. Personal stories reveal how mentorship fosters growth, adaptability, and resource sharing in this evolving field.

Join Dr. John Stewart, a Pittsburgh-based radiologist and entrepreneur behind R-Scripter, as he shares his inspiring journey from addressing his own productivity challenges to creating a widely used radiology dictation tool. He discusses the importance of user-friendly software design and overcoming business obstacles, emphasizing resilience and creativity. With insights on turning ideas into successful ventures, he debunks common entrepreneurial myths and highlights how a problem-solving mindset trumps formal education in the medical field.

Dr. Pranav Rajpurkar, a pioneer in AI at Harvard, and Dr. Michael Moritz, an abdominal radiologist from St. Louis University, dive into the future of radiology. They discuss how AI can revolutionize disease detection and improve clinical workflows, while also sharing their entrepreneurial journeys in this tech-driven field. The conversation touches on the importance of collaboration between radiologists and AI experts to achieve ambitious goals, like minimizing errors in diagnoses, and emphasizes the need for adaptability in the face of rapid technological advancements.