"There’s so much to be excited about going forward," she told Radiology Business Journal Editor Dave Pearson in an exclusive interview. 

Machine learning-based CT texture analysis can help with the evaluation of solid renal masses, according to new findings published in Academic Radiology. Could this help reduce the number of patients undergoing unnecessary surgeries?

Providers believe the digital modality could ultimately provide a more accurate cancer diagnosis compared to conventional PET, authors reported in the Journal of Nuclear Medicine.

Numerous deep learning models can detect and classify imaging findings with a performance that rivals human radiologists. However, according to a new study published in the Journal of the American College of Radiology, many of these AI models aren’t nearly as impressive when applied to external data sets.

The U.S Food and Drug Administration gave the go-ahead for two additional molybdenum-99 filling lines at the company's Columbia, Missouri, facility.

Exams of patients who require a translator appeared to be of “significantly worse” quality, underlining the need to strengthen communication, according to new research in Abdominal Imaging. 

University College London researchers harnessed this new tool to quickly analyze cardiovascular magnetic resonance images and determine the risk of other adverse events such as stroke.

AI promises to make a titanic impact on radiology, but most of the attention tends to focus on its ability to identify important findings in medical images. What about the technology’s non-interpretive qualities?