Radiology Podcast | RSNA

Dr. Lauren Kim sits down with Dr. Jonathan Kottlors to explore how large language models are reshaping radiology research and why standardized reporting is critical for trust and reproducibility. They dive into the new FLAIR framework and what it means for the future of AI integration in clinical radiology.
 
Guidelines for Reporting Studies on Large Language Models in Radiology: An International Delphi Expert Survey. Kottlors and Iuga et al. Radiology 2026; 318(2):e250913.

In this episode, Dr. Linda Chu reviews new Radiology studies exploring how photon counting CT is transforming thoracic imaging with ultra‑high resolution and dramatically lower radiation and contrast doses. She examines whether these visually striking images translate into better diagnostic confidence and meaningful improvements in patient care for emphysema and lung cancer imaging.
 
Ultra-Low-Dose Photon-counting Detector CT for EmphysemaAssessment: A Head-to-Head Comparative Study with Low-Dose CT. Yuan and Yang et al. Radiology 2026; 318(1):e251609.
 
Photon-counting CT versus Energy-integrating Detector CTin Imaging Lung Cancer. Yang et al. Radiology 2026; 318(2):e251126.
 
Photon-counting CT versus Energy-integrating Detector CTPerformance for Various BMI and Tumor Sizes in Lung Cancer. Zhou and Guo et al. Radiology 2026; 318(2):e251663.
 
Prospective Evaluation of Ultra-Low-Dose Photon-counting CT inEmphysema Assessment. Schwartz. Radiology 2026; 318(2):e254065.

Hosted by Dr. Sid Dogra, this episode of the Radiology Podcast explores new research showing that where fat is distributed in the body—particularly visceral and organ-specific fat—may matter more for brain health than overall BMI. Drawing on a large UK Biobank MRI study, Dr. Dogra discusses how specific fat distribution patterns, including pancreatic-predominant and "skinny fat" phenotypes, are associated with accelerated brain aging, cognitive decline, and increased neurologic disease risk.
 
Association of Body Fat Distribution Patterns at MRI with BrainStructure, Cognition, and Neurologic Diseases. Yu and Yao et al. Radiology 2026; 318(1):e252610.

Hosted by Dr. Ashwin Singh Parihar, this episode features Dr. Mickael Tordjman and Dr. Bachir Taouli discussing their landmark Radiology study on AI‑generated deepfake medical images that are realistic enough to fool trained radiologists. Together, they examine what this new level of image realism means for diagnostic accuracy, clinical trust, and the future of safeguards in medical imaging.
 
The Rise of Deepfake Medical Imaging: Radiologists' Diagnostic Accuracy in Detecting ChatGPT-generated Radiographs. Tordjman and Yuce et al. Radiology 2026; 318(3):e252094.
 
The Democratization of Deceit: Seeing Is No Longer Believing. Bhayana and Krishna. Radiology 2026; 318(3):e260466.

Dr. Celina Nahyun Jo talks with Dr. Mahla Radmard about how NEXUS and the Canadian C‑Spine Rule shape cervical spine imaging decisions in trauma patients. Together they explore new research that questions long‑standing imaging practices and highlights opportunities to improve value safety and accuracy in patient care.
Evaluating National Emergency X-Radiography Utilization Study and Canadian C-Spine Rule Criteria and Their Clinical Impact on Cervical Spine Imaging: Best Practice. Asadollahi and Arjmand et al. Radiology 2026; 318(1):e243834.