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

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.

In this AI-generated episode of Radiology AI Papers in a Capsule, we discuss a study that extends the NeuroHarmony AI model to address scanner variability in brain MRI for Alzheimer's disease assessment. Learn how incorporating cognitive status into harmonization may improve the reliability of quantitative imaging across diverse clinical settings.

A Machine Learning Model to Harmonize Volumetric BrainMRI Data for Quantitative Neuroradiologic Assessment ofAlzheimer Disease. Archetti and Venkatraghavan et al. Radiology: Artificial Intelligence 2025; 7(1):e240030.

Dr. Dana Alkhulaifat explores how mentorship can inspire and guide the next generation of leaders in radiology and imaging informatics. Guests Dr. Tessa Cook and Satvik Tripathi share insights on building awareness, creating opportunities, and the impact of strong mentor-mentee relationships.

Dr. Jason Adelberg talks with Dr. John Stewart about how he built rScriptor, a radiology dictation tool, from a personal side project into a widely adopted software product. Dr. Stewart shares lessons on entrepreneurship, product development, and how radiologists can turn their own ideas into real businesses.

In this episode, hosts Dr. Cody Savage and Dr. Ali Tejani speak with Dr. Pranav Rajpurkar and Dr. Michael Moritz about the future of AI in radiology, from research breakthroughs to real-world implementation. They discuss building AI models that detect every disease, the challenges of integrating AI into clinical workflows, and the entrepreneurial journey of bringing cutting-edge technology to medical imaging.

Join hosts Dr. Paul Yi and Dr. Ali Tejani as they welcome new team members Dr. Bardia Khosravi and Dr. Cody Savage to discuss the rise of AI-generated podcasts and their impact on radiology education. From Google's Notebook LM to the challenges of AI hallucinations, they explore the future of automated content creation and what it means for the Radiology Artificial Intelligence Podcast.

In this AI-generated episode of Radiology AI Papers in a Capsule, we explore a groundbreaking study on AI-integrated mammography screening. This research, conducted by Elhakim and colleagues from Odense University Hospital, examines how artificial intelligence can optimize screening workflows while maintaining diagnostic accuracy. We break down the study's three integration scenarios, their impact on workload reduction, and the clinical significance of AI-assisted double reading. Tune in for an insightful, AI-curated discussion on the evolving role of AI in radiology.

AI-integrated Screening to Replace Double Reading of Mammograms: A Population-wide Accuracy and Feasibility Study. Elhakim et al. Radiology: Artificial Intelligence 2024; 6(6):e230529.