AI algorithms can identify melanomas in dermoscopic images with an accuracy comparable to human specialists, according to research published in JAMA.

Deep convolutional neural networks (CNNs) can be trained to predict sequence types for brain MR images, according to new research published in the Journal of Digital Imaging.

Regina Barzilay, PhD, a professor at the Massachusetts Institute of Technology, has been working closely with AI technologies to improve breast cancer care ever since she was diagnosed with the disease in 2014.

A lung cancer researcher from Dartmouth College has received a $1.5 million grant from the National Cancer Institute to build and validate machine leaning approaches to develop better, more personalized cancer treatments.

The combination of machine learning and ultrasound could help healthcare providers with the diagnosis of thyroid nodules, according to new findings published in JAMA Otolaryngology.

Discussions about AI and radiology often focus on the researchers who help develop the algorithms and radiologists themselves. But a new analysis published in Academic Radiology shines a light on another key role in the implementation of AI: the imaging informaticist.

Deep learning neural networks can improve the detection of tuberculosis (TB) and provide health systems with considerable cost savings, according to new findings published in Scientific Reports.

AI can detect brain hemorrhages in CT scans more accurately than some radiologists, according to new findings published by Proceedings of the National Academy of Sciences.