Artificial intelligence (AI) is becoming an essential part of radiology, meaning the industry must consider ethics for computers and AI, according to an opinion piece by Marc Kohli, MD, and Raym Geis, MD, published July 15 in the Journal of the American College of Radiology.  

A machine learning algorithm trained to analyze MRI images identified schizophrenia patients with 78 percent accuracy, according to a recent study published in Molecular Psychiatry.

Researchers used single-photon emission computed tomography (SPECT) to assess the immune system recovery of macaque monkeys with an HIV-like infection. The work may help researchers understand recovery in humans after HIV treatment.

Researchers have developed a fully integrated computer-aided diagnosis (CAD) system that detects, segments and classifies masses from mammograms using deep learning and a deep convolutional neural network (CNN), according to a new study published by the International Journal of Medical Informatics.

A team of U.S. researchers has uncovered the exact point when normal, healthy protein becomes toxic but has yet to form tangles. Experts believe the findings could be a target for developing Alzheimer's treatments.

An integrated computer-aided diagnosis (CAD) system developed by researchers from Kyung Hee University and Sungkyunkwan University in South Korea could outperform current conventional deep learning methodologies used by radiologists to detect, segment and classify tumors from digital x-ray mammograms.

The American College of Radiology Data Science Institute (ACR DSI) announced this week that it is co-sponsoring a National Institute of Biomedical Imaging and Bioengineering (NIBIB) workshop about artificial intelligence (AI) in medical imaging.

A 3D color medical scanner invented by father and son scientists in New Zealand recently imaged its first human subject, according to a news release from the University of Canterbury in New Zealand.