Researchers from the University of North Carolina have called for more rigorous testing of artificial intelligence (AI)-powered medical devices, following a comprehensive study of nearly three decades of U.S. Food and Drug Administration (FDA) authorizations.
Some devices used simulated images, not real patient data, which technically didn’t qualify as testing in real patients, also known as clinical validation. Although AI medical devices serve many useful purposes, including detection of cancer and strokes on radiology scans, this study shows they also bring with them potential dangers. “We shared our findings with directors at the FDA who oversee medical device regulation, and we expect our work will inform their regulatory decision making,” Sammy Chouffani El Fassi, a doctor of medicine candidate at the University of North Carolina Medical School and first author, said in an interview with The Epoch Times.
The study, completed in about 18 months, included eight authors, as well as a large team of consultants from academic institutions and corporations. Their analysis revealed that only 56 percent of approved devices had this validation. After analyzing FDA authorizations from 1995 to 2022, researchers recommended establishing a “gold-standard indicator” of safety and effectiveness.
Most authorized devices were for radiology, with 75 percent in this category. Nearly all were classified as intermediate-risk class II devices. Class II devices include diagnostic dev.