The need for speed | How AI can help radiologists learn from the past | Trends in AI-based radiology research

Most parents of pediatric patients undergoing imaging examinations want those results as quickly as possible, even if it means not discussing them in person with a physician, according to new findings published in Academic Radiology.

Machine learning models can be trained to learn from how radiologists make decisions when interpreting screening mammograms, according to a new study published in the Journal of Digital Imaging. Such research may have a significant impact if used to train specialists.

The United States is a global leader in publishing AI-based radiology research, according to new findings published in the American Journal of Roentgenology.

Women with breast arterial calcifications (BACs) are at an increased risk of cardiovascular disease (CVD). According to a new analysis published in the European Journal of Radiology, this opens the door for mammograms to screen patients for both breast cancer and CVD at once.

Chronic active lesions visible on brain MRI scans, identifiable by their “darkened outer rims,” are associated with multiple sclerosis, according to new findings published in JAMA Neurology.

Radiologists’ pay-raise rates are flat. Administrators are working hard to earn bonuses. Lots of technologists are looking for new opportunities, and few PACS pros are skimping on time off (but not many are overdoing it either). See the numbers behind the factoids in the tabulated results of the 2019 RBJ Salary Survey.

Point-of-Care ultrasound is now routinely used wherever a patient in need happens to be. In the midst of it all, radiologists and ER doctors are looking for ways to collaborate rather than compete.