The FDA has OK’d two subsidiaries of Los Angeles-based RadNet to sell medical AI software—one product for diagnosing breast cancer, the other for streamlining MRI prostate reporting workflows. 

Combining ensemble AI models with reads from breast radiologists of mixed experience levels can help health systems consistently diagnose malignant architectural distortion on mammography.

“We know that premature babies have a better chance of survival if they get a high quality of care in the first month of birth,” Jong-Hoon Kim, associate professor at the Washington State University School of Engineering and Computer Science and a co-corresponding author on the study, said in a statement.

More than 40% of Americans are generally OK with the thought of AI reading their chest x-rays. Moreover, some 12.3% are very comfortable with the prospect.

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.