San Francisco-based tech company Bay Labs this week announced the success of its deep learning software, EchoMD AutoEF, in reducing variability in cardiovascular imaging.

As artificial intelligence (AI) and medical imaging continue to transform clinical practice, radiologists-in-training can no longer take a passive role in the march toward this coming change.

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.

A new study from Queen’s University Belfast researchers found the eye may be a critical indicator for Alzheimer’s disease (AD) along with a host of other neurodegenerative diseases.

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?

An MR framework enabling simultaneous multiple parametric T1 and T2 proton density mapping—MR fingerprinting—can identify lesions indicative of a severe neurological condition in patients with a common form of epilepsy—all in under 150 seconds.

Researchers found that using a virtual reality (VR) headset was able to identify brain aneurysms with the same accuracy as matched reference standards, according to a study published in the online July issue of Clinical Neurology and Neurosurgery.

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.