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.

The Machine Learning Challenge on Pneumothorax Detection and Localization will kick-off at the SIIM 2019 Annual Meeting starting June 26 in Aurora, Colorado.

“With information obtained from this study, it is possible that neuroimaging in newborns may to some extent predict neurodevelopment even for healthy children, and prenatal intervention targeted at improving white matter integrity at birth will be important for further promoting neurodevelopment in children,” wrote researchers of a new Radiology study.

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.

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.

Aaron Williams was 16 years old when he committed suicide on the campus of his Charleston, South Carolina, high school in 2010. It was only until after the tragedy that neuroimaging revealed multiple lesions in his brain.

Machine learning can reduce a radiologists workload by lowering the number of screening mammograms they’re required to read while preserving accuracy, according to results of a feasibility study published in the Journal of the American College of Radiology.

Radiology software supplier Intelerad Medical Systems is investing $75 million to develop new artificial intelligence and cloud-based offerings.