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.

Mechanical engineers and surgeons from the University of Hong Kong have recently developed what could be the world's first neurosurgical robotic system that can perform bilateral stereotactic neurosurgery on a patient inside an MRI machine.

A convolutional neural network (CNN) model performed as well as clinicians in classifying the area of concern in retinal fundus images and provided evidence for why those choices were made—a common problem for artificial intelligence (AI) technology.

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

MIT researchers have developed an artificial intelligence-(AI) based algorithm that can register three-dimensional (3D) images 1,000 times more quickly than standard medical image registration techniques.