Machine learning can help reduce a radiologist’s workload by identifying negative mammograms that do not need to be interpreted, according to new findings published in the Journal of the American College of Radiology.

Machine learning could be a real game-changer for interventional radiology (IR) in the years ahead, according to a new analysis published in the American Journal of Roentgenology.

The FDA is responsible for regulating AI solutions designed and developed to provide care for patients, a task that leads to certain unique challenges.

Numerous healthcare organizations have released a second research roadmap focused on AI technologies in radiology. The full document was published in the Journal of the American College of Radiology.

Augmented datasets can improve the overall accuracy of deep convolutional neural networks (DCNNs), according to new findings published in Clinical Radiology.

Machine learning can help improve the overall performance of CT scans, reducing radiation exposure and boosting image quality, according to new findings published in Nature Machine Intelligence.

The Society for Imaging Informatics in Medicine (SIIM) and American College of Radiology (ACR) are hosting a new machine learning challenge as part of a collaboration with the Society of Thoracic Radiology (STR) and MD.ai.

Businessman Vinod Khosla, co-founder of Sun Microsystems and a longtime venture capitalist, said this week that he thinks any radiologist still practicing in 10 years “will be killing patients every day.”