MSK AI Panel at RSNA 2024
The MSK AI Panel at RSNA 2024 was moderated by Avneesh Chhabra, MD (UT Southwestern), a globally recognized expert in MSK radiology and a pioneer in MR neurography with numerous published articles. The panel featured James J. Xia, M.D., Ph.D. (United Imaging), Daniel F. Jones (Gleamer), Alexis Guignard (AZmed), and Darren Stephens (Radiobotics).
The discussion focuses on AI-powered fracture detection tools, their accuracy, impact on workflow efficiency, and use cases in radiology.
Topics include:
- AI-powered fracture detection tools
- Accuracy, sensitivity, and specificity
- Generalizability across different populations and fracture types
- Efficiency benefits, including reading time and report turnaround time
- Use cases in Radiology, Orthopaedics, and Emergency
The key discussion points:
Advanced AI-powered tools improve fracture detection by identifying acute, subacute, and chronic fractures with high sensitivity and specificity while addressing challenges like differentiating benign from pathological lesions.
- Radiologists benefit from significant time savings, as these solutions reduce reading times and improve overall efficiency in high-volume settings such as emergency departments.
- New applications, including automated scoliosis assessments, limb alignment measurements, and tools for hip replacement planning, enhance precision and streamline workflows.
- Real-world studies highlight the ability of these AI applications to minimize missed fractures, improve diagnostic accuracy, and optimize patient outcomes.
This video was organized and recorded by CARPL.ai at RSNA 2024. You can see more videos from CARPL at the @CARPLai Youtube channel or learn more at the CARPL website.
