Three Decades of Food and Drug Administration Authorizations of Artificial Intelligence/Machine Learning-Enabled Medical Devices: Persistent Specialty Concentration and the Care-Delivery Gap (1995-2025).
Authors
Affiliations (2)
Affiliations (2)
- Interventional Radiology, Kaiser Permanente Los Angeles Medical Center, Los Angeles, USA.
- Radiology, University of California, Riverside, Riverside, USA.
Abstract
Background The US Food and Drug Administration (FDA) maintains a public list of artificial intelligence (AI) and machine learning (ML)-enabled medical devices that have received marketing authorization. Prior published analyses examined this list at earlier time points and reported a marked dominance of radiology applications. Methodology We performed a longitudinal descriptive analysis of all 1,430 AI/ML-enabled medical device authorizations recorded by the FDA between September 1995 and December 2025 to characterize the cumulative growth, specialty distribution, and manufacturer concentration of authorized devices. Results The annual authorization volume increased from a mean of 1.8 per year between 1995 and 2014 to 264 per year between 2023 and 2025, with 331 authorizations recorded in 2025 alone. Devices reviewed by the FDA's Radiology panel accounted for 1,094 of 1,430 authorizations (76.5%), and the three most represented panels (Radiology, Cardiovascular, and Neurology) accounted for 90.6% of all authorizations. Several large clinical specialties were represented by very small numbers of authorized devices, including Pathology (n = 9, 0.6%), Microbiology (n = 6, 0.4%), and Obstetrics and Gynecology (n = 4, 0.3%). No authorizations were recorded under a psychiatry or behavioral health review panel. Of 740 unique companies, 502 (67.8%) had a single authorized device, while 13 (1.8%) companies accounted for 247 (17.3%) devices. Conclusions The cumulative regulatory record demonstrates rapid growth that has been concentrated in image-rich diagnostic specialties, with limited representation across many specialties that account for substantial clinical activity in the United States. These findings may inform policy discussions about where regulatory, infrastructure, and dataset investments are most needed to broaden the clinical scope of medical AI.