Multicenter research revealed that a combined model of clinical factors and ultrasound-based radiomics exhibited greater than a 23 percent higher per patient-level accuracy rate for identifying HER2 breast cancer than a clinical model.

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.

Researchers noted the combination of AI and bpMRI had an average AUC of 91 percent for csPCa detection in contrast to 78 percent for unassisted radiologist interpretation in a recent meta-analysis.

In the second episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss recent studies published by the Journal of the American Medical Association (JAMA) that suggested moving to more of a risk-adapted model for mammography screening.

In a study involving over 24,000 women, two-thirds of whom had dense breasts, adjunctive AI led to a significantly higher rate of cancer detection for breast radiologists with no difference in recall rates.

Amid the variety of different state CME requirements that need to be navigated for teleradiologists, there are certain CME topics that have no relevance for radiology.