The AI software assisted in various tasks, such as segmenting, labeling and measuring normal structures, providing an automated analysis of pulmonary, cardiac and musculoskeletal findings.

Testing AI’s ability to detect carotid artery disease on ultrasound, U.K. researchers have found their algorithm achieved 90% accuracy, along with 87% sensitivity and 82% specificity, at the task.

A multi-institutional image-data repository launched to support AI-based research into COVID-19 has been the beneficiary of more than 30,000 anonymized imaging files from the Radiological Society of North America alone.

Researchers have combined three emerging technologies to detect and classify breast cancers found in follow-up imaging of women whose recent screening mammography was deemed normal.

AI can help humans inspect chest X-rays for COVID-19, but the technology is unfit to serve as a standalone diagnostic tool for that purpose. 

The Internet is an acceptable source of images for training algorithms to automatically triage patients with dislocated joints and similar orthopedic emergencies. 

As FDA-approved AI software continues to proliferate in radiology—well more than 150 products to date and rising—a trio of Yale radiologists has compiled a status report focused on AI applications available to, specifically, emergency radiology.

AI can safely and accurately identify healthy breast tissue on ultrafast breast MRI, negating the need for a radiologist’s closer look and, in the process, lowering cancer screening costs and widening patient access to breast MRI.