As researchers continue to make significant advances with artificial intelligence (AI) and deep learning, has the time come for radiologists to be concerned about their jobs?

GE Healthcare’s Deep Learning Image Reconstruction (DLIR) engine, designed to be used with its Revolution Apex CT solution, has gained FDA clearance.

The supply of radiation oncologists hasn’t kept up with the global demand for radiation therapy. But could experts from across the world help create an AI algorithm capable of closing that gap?

An AI model built by researchers at the Massachusetts Institute of Technology helps robots better predict how they’ll interact with solid objects and liquids, improving their ability to mold deformable materials.

A detailed roadmap outlining research priorities for artificial intelligence (AI) in radiology was published April 16 in Radiology, and the organizations involved have announced that a second report is due later this year.   

The human-level success of deep learning has made some in medicine question whether automation may eventually take over many tasks performed by radiologists. An author, and radiologist, put that question to bed in an April 18 editorial published in the Journal of the American College of Radiology.

Dopamine transporter (DAT) SPECT may be a useful imaging biomarker to determine the severity of Parkinson’s disease, according to an April 17 study published in the American Journal of Roentgenology.

“Our goal was to provide a blueprint for professional societies, funding agencies, research labs, and everyone else working in the field to accelerate research toward AI innovations that benefit patients," wrote lead author, Curtis P. Langlotz, MD, PhD.