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.

Machine learning algorithms can now identify lymphedema—a chronic side effect of breast cancer treatment—with 94 percent accuracy, New York University researchers reported this month in mHealth.

Radiology professionals working on artificial intelligence (AI) technologies can learn a lot from studying self-driving vehicles, according to a new commentary published in the Journal of the American College of Radiology.

Convolutional neural networks (CNN) were more accurate than even the most expert dermatologists in detecting skin cancer, researchers reported in Annals of Oncology this week.

Machine learning techniques perform well when tasked with predicting malignancy in breast lesions identified during breast cone-beam CT (CBCT) exams, according to a new study from German researchers published by the American Journal of Roentgenology. One technique, back propagation neural networks (BPN), outperformed two radiologists.

British Prime Minister Theresa May announced this week she will pledge millions of pounds to the fight against cancer through the development of artificial intelligence (AI), Forbes has reported.

A new partnership between University College London Hospitals and the Alan Turing Institute aims to start using artificial intelligence (AI) to perform certain tasks typically carried out by doctors and nurses.