What follows is the second part of our coverage of the "Radiology: Artificial Intelligence Fireside Chat" conducted at RSNA 2021. The in-depth discussion, for which excerpts are presented here, was well-facilitated by Dania Daye, MD, PhD, Massachusetts General Hospital/Harvard Medical School; and Paul Yi, MD., University of Maryland School of Medicine; with RSNA Journal Radiology: AI Editor Charles E. Kahn, Jr., MD, MS, Perelman School of Medicine, University of Pennsylvania. Featured panelists included: John Mongan, MD, PhD, University of California, San Francisco; Jayashree Kalpathy-Cramer, MS, PhD, Athinoula A. Martinos Center for Biomedical Imaging; and Linda Moy, MD, NYU Grossman School of Medicine. Q: The successes we have seen in AI are clear. There is cutting-edge research emerging, but with every success, we are identifying multiple obstacles.

These days, a radiologist at UCSF will go through anywhere from 20 to 100 scans a day, and each scan can have thousands of images to review. These days, a radiologist at UCSF will go through anywhere from 20 to 100 scans a day, and each scan can have thousands of images to review. In health care, you could say radiologists have typically had a pretty sweet deal. They make, on average, around $400,000 a year -- nearly double what a family doctor makes -- and often have less grueling hours. But if you talk with radiologists in training at the University of California, San Francisco, it quickly becomes clear that the once-certain golden path is no longer so secure.