Clinical Report: FDA Change Plans in Radiology AI
Overview
The adoption of Predetermined Change Control Plans (PCCPs) has increased among FDA-cleared radiology AI devices following new FDA guidance.
Background
The integration of artificial intelligence (AI) in radiology is rapidly evolving. The FDA's introduction of PCCPs aims to enhance the oversight of AI/ML devices, particularly in the context of continuous performance monitoring.
Data Highlights
| Year | Devices Cleared with PCCPs | AI/ML Devices |
|---|---|---|
| 2020-2025 | 170 | 34 (92%) |
| 2024 | - | 4 |
| 2025 | - | 22 (65%) |
Key Findings
- 1,394 FDA-listed AI/ML submissions were evaluated, with 1,080 (78%) in radiology.
- 130 (15%) of 870 unique radiology devices underwent sequential 510(k) clearances.
- The mean interval between sequential submissions decreased from 25 months to 13 months post-2021.
- Public documentation had a mean score of 5 on an 8-point rubric assessing transparency.
- Only 3 devices provided details on postmarket surveillance in public summaries.
- Documentation scores improved from 4 in 2024 to 5 in 2025, but completeness did not differ significantly before and after guidance.
Clinical Implications
Healthcare professionals should be aware of the increasing adoption of PCCPs in radiology AI devices.
Conclusion
The findings indicate a growing trend in the adoption of PCCPs among radiology AI devices.
Related Resources & Content
- Radiology: Artificial Intelligence, 2026 -- Predetermined Change Control Plan Adoption and Documentation Transparency in U.S. Food and Drug Administration-cleared Radiology Artificial Intelligence/Machine Learning Devices.
- MDSpire News — Radiology AI in Routine Practice
- European Radiology — A Comprehensive Guide to the Role of Artificial Intelligence in Thoracic Imaging: Insights from the European Society of Thoracic Imaging (ESTI)
- Journal of Medical Internet Research (JMIR) — Patients’ Perspectives on the Implementation of AI in Radiological Diagnostics: Focus Group Study
- The Role of Artificial Intelligence in Radiology: A Comprehensive Review of Current Workflow Automation, Diagnostic Accuracy, and Future Efficiency Enhancements
- Radiology AI in Routine Practice
- A Comprehensive Guide to the Role of Artificial Intelligence in Thoracic Imaging: Insights from the European Society of Thoracic Imaging (ESTI)
- Patients’ Perspectives on the Implementation of AI in Radiological Diagnostics: Focus Group Study
- ACR Approves First Practice Parameter for Imaging Artificial Intelligence
- Predetermined Change Control Plan Adoption and Documentation Transparency in U.S. Food and Drug Administration-cleared Radiology Artificial Intelligence/Machine Learning Devices.
- Clinical Implementation of AI for Pulmonary Embolism Detection in over 30 000 CT Pulmonary Angiography Examinations | Radiology: Artificial Intelligence
This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.
