AI can triage certain mammograms | A new way to battle burnout | Readability of online patient materials

Using deep learning models to triage screening mammograms can improve radiologist specificity without hurting sensitivity, according to new research published in Radiology.

Physician burnout continues to be a problem in the United States, and numerous studies have highlighted its impact on radiologists. 

Content featured on RadiologyInfo.org, a resource sponsored by the American College of Radiology and RSNA, is still too complex for all patients to understand, according to new findings published in the American Journal of Roentgenology.

The FDA has published a new guidance document focused on making improvements to the overall safety of MR environments and MRI-related medical device labeling.

Artificial intelligence (AI) has been one of the most hyped advancements in radiology for many years now, sparking vigorous debates about its potential impact on patient care. But early on, discussions about AI were more theoretical than factual; it was simply too early to know what the future may bring.

It’d be a stretch to say Walmart has gone into the radiology business. But there’s no denying that the retail behemoth has made a business decision having everything to do with radiology.

In 2017, some 60 percent of physicians reported being “not at all familiar” or “slightly familiar” with MACRA. A meager 8 percent said they were “very familiar” with the legislation. Two years later, anecdotal evidence strongly suggests the lack of understanding persists.