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.

Live recording of the RSNA 2024 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
• Linda Moy, MD - Editor of Radiology
• Nina Kottler, MD, MS - Associate CMO, & Clinical AI VP Clinical Operations, Radiology Partners
• Matthew Lungren, MD, MPH - Chief Data Science Officer for Microsoft Health & Life Sciences
• Woojin Kim, MD - Chief Medical Officer, ACR Data Science Institute & Chief Medical Information Officer of Rad AI

Dr. Ali Tejani and Dr. Paul Yi preview RSNA 2024 with Dr. Charles Kahn, Editor-in-Chief of Radiology AI. They discuss key highlights from the past year, including the updated CLAIM guidelines, innovative AI tools, and upcoming sessions at RSNA2024. 

Dr. Ali Tejani and Dr. Paul Yi discuss the current state of large language model (LLM) research and its applications in radiology with their guests Dr. Merel Huisman, Dr. Tugba Akinci D'Antonoli, and Dr. Christian Bluethgen.  They explore the rapid evolution of this field, including the surge in publications and the diverse use cases being explored.

A New Era of Text Mining in Radiology with Privacy-Preserving LLMs. Akinci D'Antonoli and Bluethgen.  Radiology: Artificial Intelligence 2024; 6(4):e240261.

The AI Generalization Gap: One Size Does Not Fit All. Huisman and Hannink. Radiology: Artificial Intelligence 2023; 5(5):e230246.

Dr. Ali Tejani and Dr. Paul Yi discuss the current state of large language model (LLM) research and its applications in radiology with their guests Dr. Merel Huisman, Dr. Tugba Akinci D'Antonoli, and Dr. Christian Bluethgen.  They explore the rapid evolution of this field, including the surge in publications and the diverse use cases being explored.

A New Era of Text Mining in Radiology with Privacy-Preserving LLMs. Akinci D'Antonoli and Bluethgen.  Radiology: Artificial Intelligence 2024; 6(4):e240261.

The AI Generalization Gap: One Size Does Not Fit All. Huisman and Hannink. Radiology: Artificial Intelligence 2023; 5(5):e230246.