Evaluating Transparency of Predetermined Change Control Plans in FDA-Cleared Radiology AI Devices: A Systematic Scoping Review

Background Adoption of FDA-cleared AI/ML-enabled devices is rising rapidly in Radiology. By recently denying a petition to exempt computer-aided detection (CAD) devices from 510(k) review, the FDA reaffirmed Predetermined Change Control Plans (PCCPs) as the preferred framework for managing post-market modifications. Purpose To characterize PCCP adoption and documentation transparency among FDA-cleared radiology AI/ML-devices. Materials and methods Cross-sectional systematic scoping review (PRISMA-ScR; PROSPERO) using linked data from FDA AI/ML-enabled device databases through April 2026. PCCP documentation completeness scored using an 8-point rubric derived from FDA's final PCCP guidance (December 2024). Results Among 1,394 FDA-listed AI/ML- device submissions, 1080 (77.5%, 870 unique devices) were radiology submissions, nearly all cleared via 510(k). Across all FDA panels, 170 devices were PCCP-cleared; radiology led AI/ML-specific PCCP adoption at 91.2% (34/37 radiology PCCP devices). Of 34 PCCP-cleared radiology AI devices, 65% (22/34) cleared in 2025 alone following final FDA guidance. Discrepancies between FDA's public database and individual summaries required manual adjudication for 27% of devices (9/34). PCCP documentation scores ranged from 0–8 (mean 5), with most modifications focused on data retraining, compatibility expansion and algorithm optimization. Continuous monitoring of device performance and predefined drift triggers for re-training were absent from public summaries. Conclusion PCCP adoption is accelerating in Radiology, yet public lifecycle controls, particularly monitoring performance metrics and trigger thresholds, remain sparse. Standardized PCCP reporting of lifecycle controls must be required as a condition of PCCP authorization to enable systematic post-market monitoring, as this pathway scales.