The reception was similarly warm among parents toward AI used to assess a child’s need for antibiotics or bloodwork.

A Silicon Valley AI shop has been OK’d by the FDA to market software that automatically flags suspected pulmonary embolisms (PEs) and immediately notifies physicians.

Researchers have demonstrated a deep learning model that can correct course for breast radiologists who otherwise may have erroneously deemed tissue dense in screening exams.

Computer-aided detection boosted by AI has often proven superior to traditional CAD over the past decade, yet the “new way” has been slow to win broad adoption.

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.

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.