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.