Convolutional neural networks (CNNs) can be trained to analyze visual imagery much easier than other artificial neural networks, making their capabilities especially important to the future of radiology, according to a new analysis published in Current Problems in Diagnostic Radiology.

To gain a better understanding of how the industry perceives radiology’s maturing relationship with artificial intelligence, researchers surveyed attending radiologists and trainees at a single diagnostic radiology (DR) residency program in August 2017, publishing their findings in the Journal of the American College of Radiology.

A combination of machine learning and quantitative computed tomography (CT) could predict the eventual onset of bronchiolitis obliterans syndrome (BOS), also known as popcorn lung, in patients receiving lung transplants, researchers reported in Academic Radiology this week.

Artificial intelligence (AI) is one of the biggest topics in healthcare today, and the authors of a recent analysis published in the Journal of the American College of Radiology wrote at length about radiology’s role in the development and implementation of these state-of-the-art technologies.

Arterys, a San Francisco-based healthcare company focused on cloud-based medical imaging technology, announced Thursday, Feb. 15, that it has received FDA clearance for its Arterys Oncology AI suite.

The FDA announced late Tuesday, Feb. 13, that it has approved marketing of Contact, a clinical decision support software that analyzes CT results and notifies providers of potential strokes. Contact was developed by Viz.ai, a San Francisco-based healthcare company that specializes in artificial intelligence (AI) and deep learning technologies.

The American College of Radiology (ACR) Data Science Institute (DSI) is on a mission to implement artificial intelligence (AI) into clinical practice, according to a new analysis published in the Journal of the American College of Radiology.

After the U.S. Food and Drug Administration approved computer-aided diagnosis (CAD) in mammography in 1998 and CMS approved reimbursement for CAD in 2002, there was a lot of optimism about the future of this advanced technology. So what went wrong?