A machine learning tool developed by researchers at Imperial College London could assess the severity of leukoaraiosis in stroke patients with greater efficiency and accuracy than the typical emergency room CT, a study published this week in Radiology states.

As the relationship between radiology and artificial intelligence (AI) continues to evolve, radiology trainees may find themselves wondering what, exactly, they should know about these groundbreaking technologies. Do they need to be AI experts? Can they just avoid the subject altogether?

RSNA announced this week that it will be offering a new Spotlight Course focused on artificial intelligence (AI) September 23-24 at the Espace Saint-Martin in Paris.

Radiologists are “significantly influenced” by contextual bias when interpreting mammograms, according to a new study published in the Journal of Medical Imaging.

Researchers from the Massachusetts Institute of Technology (MIT) in Cambridge have been studying a machine learning algorithm they say makes the process of medical image registration more than 1,000 times faster.

Mercy Radiology, a New Zealand-based imaging provider, has plans to use artificial intelligence (AI) algorithms to help with the detection of prostate cancer.

A machine learning-based “red dot” triage system could help differentiate between normal and abnormal chest radiographs while optimizing clinician workflow, British researchers reported this month in Clinical Radiology.

The American Society of Neuroradiology (ASNR) announced that Peter Chang, MD, a neuroradiology fellow at the University of California San Francisco, has received the Cornelius G. Dyke Memorial Award for his recent research involving deep learning technologies.