The Future of Breast Cancer Screening with AI
Radiology Advances Podcast | RSNA
Release Date: 07/16/2025
The Robotic Edge in CT-Guided Procedures
Radiology Advances Podcast | RSNA
A prospective randomized trial compares robotic versus manual needle insertion for CT-guided intervention. Host commentary summarizes the results that show the robotic system matched manual accuracy and clinical success rates while significantly reducing radiation exposure to the interventionalist. The discussion touches on clinical implications for workflow, safety, and the evolving role of robotics in interventional radiology.
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AI-Powered Precision in MRI with MRAnnotator
Radiology Advances Podcast | RSNA
MRAnnotator is a deep learning model that segments 44 anatomical structures across diverse MRI sequences. Developed at Mount Sinai, it shows strong generalizability across scanners and sites, outperforming existing models. Host commentary summarizes the model development and datasets and explores its impact on AI development, annotation workflows, and multi-center research.
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The Future of Breast Cancer Screening with AI
Radiology Advances Podcast | RSNA
This episode explores a groundbreaking study from Radiology Advances evaluating the use of artificial intelligence as a second reader in screening mammography. Host commentary highlights how the AI-assisted workflow improved cancer detection, reduced radiologist workload, and enhanced reading efficiency, while also emphasizing the importance of thoughtful integration into clinical practice.
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Non-Contrast Dual-Energy CT for PE Detection
Radiology Advances Podcast | RSNA
In this ai generated episode of the Radiology Advances Podcast, we explore an innovative approach to detecting pulmonary embolism using dual-energy CT without intravenous contrast. Learn how electron density and Z-effective maps could offer a new option for patients with contraindications to contrast media.
info_outlineThis episode explores a groundbreaking study from Radiology Advances evaluating the use of artificial intelligence as a second reader in screening mammography. Host commentary highlights how the AI-assisted workflow improved cancer detection, reduced radiologist workload, and enhanced reading efficiency, while also emphasizing the importance of thoughtful integration into clinical practice.