ACR Launches AI e-Learning Hub

For women with intermediate risk and a personal history of breast cancer, an emerging AI system offered an 81 percent AUC for breast cancer detection, according to new research.

In the third episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss the challenges of expanded breast cancer screening amid a backdrop of radiologist shortages and ever-increasing volume on radiology worklists.

In light of increasing imaging volume, retirements and burnout in radiology, this author suggests that technologies such as portable X-ray devices may have a significant impact.

Richard Duszak, MD, and Mina Makary, MD, discuss a number of issues, ranging from demographic trends and NPRPs to physician burnout and medical student recruitment, that figure to impact the radiology workforce now and in the near future.

Text messaging may go a long way toward reducing the costs and potential health repercussions that can occur with cancellations, no-shows and no-gos in radiology practice.

For patients with newly diagnosed obstructive coronary artery disease (CAD), a multimodal machine learning model offered an 86 percent AUC for predicting MACE, which was 10 percent higher than CCTA alone and over 35 percent higher than the Framingham Risk Score.