Discussions about AI and radiology often focus on the researchers who help develop the algorithms and radiologists themselves. But a new analysis published in Academic Radiology shines a light on another key role in the implementation of AI: the imaging informaticist.

Deep learning algorithms can be trained to flag suspicious chest x-rays in an emergency department (ED) setting, according to new research published in Radiology.

The combination of machine learning and ultrasound could help healthcare providers with the diagnosis of thyroid nodules, according to new findings published in JAMA Otolaryngology.

Working alongside machine learning technology can help radiologists detect more breast cancers, according to new findings published in IEEE Transactions on Medical Imaging.

CMS has announced a new plan to “modernize” Medicare and find new ways to “protect taxpayers from fraud, waste and abuse.” Could AI technologies play key role in the agency’s strategy?

Deep learning neural networks can improve the detection of tuberculosis (TB) and provide health systems with considerable cost savings, according to new findings published in Scientific Reports.

AI can detect brain hemorrhages in CT scans more accurately than some radiologists, according to new findings published by Proceedings of the National Academy of Sciences.

Researchers have trained a machine learning model to identify patients with familial hypercholesterolemia (FH), a genetic disorder that increases a person’s risk of coronary artery disease.

Deep learning can be used to predict the future hospitalization of pediatric patients, according to new research published in the American Journal of Managed Care.