Canadian researchers working with Toronto General Hospital-University Health Network have developed a natural language processing (NLP) approach to predicting downstream radiology resource utilization, according to work published in the Journal of the American College of Radiology March 2.

As new hardware and software are introduced into interventional suites, imaging labs and surgical theaters, who bears responsibility for bringing medical personnel up to speed on the advantages they could deliver for safety and efficacy?

Coronary artery calcium scanning is not a Magic 8 Ball, but it is a powerful predictor and a valuable tool for cardiology practices.

The FDA on March 7 granted Breakthrough Device designation to Paige.AI, a year-old New York startup that’s using AI to help diagnose and treat cancer, the company announced.

The rapid advancement of AI technologies has left the medical community in a state of flux, unsure of where to direct their efforts to deliver the best, most effective care. But to one dean the answer is simple: prioritize patients.

Deep learning is an important element of AI that’s helping advance diagnostics and treatment, but it also remains relatively uncharted territory.

A collaboration between Boston-based pharmaceutical startup Berg and French company Sanofi could one day result in more precise flu vaccines and even personalized shots tailored to individuals’ unique immune systems, Time reported March 5.

Though the idea of artificial intelligence displacing radiologists worries more than half of surveyed medical students interested in an imaging career, radiology programs have seen a spike in applications in recent years, according to work published in Academic Radiology.