Along with radiology-specific software and products, the list may include newly greenlit offerings for use in other settings that are increasingly important to multidisciplinary care.

Some 81% of the models—70 of 86 DL algorithms reported in 83 separate studies—diminished at least somewhat in diagnostic accuracy compared with their accuracy on internal datasets.

Ami Bhatt, MD, the American College of Cardiology (ACC) chief innovation officer and adult congenital heart disease cardiologist at Mass General Hospital, discusses how to get physician acceptance to use artificial intelligence (AI). 

Competing to classify thyroid nodules on ultrasound images as either malignant or benign, three deep learning models have essentially drawn a tie with four radiologists.

Researchers from Boston Children’s Hospital were able to predict with 83% accuracy if patients were going to be a no-show at the time of their appointment.

After being presented with more than 15,000 images, each DL network yielded results comparable to that of four seasoned radiologists, authors of a new EJR study said.

Mayo Clinic researchers have demonstrated a deep learning model that can automatically segment kidneys and measure total kidney volumes using only 3D ultrasound images.

Experts have developed an artificial intelligence model that can estimate gestational age with accuracy that rivals that of formally trained sonographers completing fetal biometry scans.