Abstract
Background: The US Food and Drug Administration (FDA) authorized over 690 machine learning (ML)-enabled medical devices between 1995 and 2023. In 2024, new guidance enabled the inclusion of Predetermined Change Control Plans (PCCPs), raising expectations for transparency, equity, and safety under the Good Machine Learning Practice (GMLP) framework. Objective: The objective was to assess regulatory pathways, predicate lineage, demographic transparency, performance reporting, and PCCP uptake among ML-enabled devices approved by the FDA in 2024. Methods: We conducted a cross-sectional analysis of all FDA-authorized ML-enabled devices in 2024. Data extracted from FDA summaries included regulatory pathway, predicate genealogy, performance metrics, demographic disclosures, PCCPs, and cybersecurity statements. Descriptive and nonparametric statistics were used. Results: The FDA authorized 168 ML-enabled Class II devices in 2024. Most (94.6%) were cleared via 510(k); 5.4% were cleared via De Novo. Radiology dominated (74.4%), followed by cardiovascular (6.5%) and neurology (6.0%). Non-US sponsors accounted for 57.7% of clearances. Among 159 510(k) devices, 97.5% cited an identifiable predicate; the median predicate age was 2.2 years (IQR 1.2-4.1), and 64.5% ML-enabled. Predicate reuse remained uncommon (9.9%). Median review time was 162 days (151 days for 510(k) vs. 372 days De Novo; p < 0.001). A total of 49 devices (29.2%) reported both sensitivity and specificity; 15.5% provided demographic data. PCCPs appeared in 16.7% of summaries, and cybersecurity considerations appeared in 54.2%. Conclusions: While 2024 marked a record year for ML-enabled device approvals and internationalization, uptake of PCCPs and transparent performance and demographic reporting remained limited. Policy efforts to standardize disclosures and strengthen post market oversight are critical for realizing the promises of GMLP.