Less than 30% of FDA-cleared AI devices shared key safety and adverse event data before approval, raising regulatory concerns.
Key Details
- 1A new analysis reviewed FDA approval and recall databases for all AI/ML devices cleared from 1995 to July 2023.
- 2Of 691 devices analyzed, 531 targeted radiology applications.
- 3Fewer than 30% of devices provided data on safety, efficacy, or adverse events prior to approval.
- 4No predefined efficacy or safety reporting standards currently exist for AI/ML devices in the U.S., unlike with pharmaceuticals.
- 5The findings highlight a regulatory gap and call for stricter testing and dedicated approval pathways for AI/ML-based medical devices.
Why It Matters
This research underscores a major gap in regulatory standards and transparency for AI devices in radiology, potentially impacting patient safety and trust. Addressing these concerns could lead to more robust evidence and safer adoption of AI in clinical imaging practice.
Source
Radiology Business
Related News
•AuntMinnie
Toronto Study: LLMs Must Cite Sources for Radiology Decision Support
University of Toronto researchers found that large language models (LLMs) such as DeepSeek V3 and GPT-4o offer promising support for radiology decision-making in pancreatic cancer when their recommendations cite guideline sources.
•AuntMinnie
AI Model Using Mammograms Enhances Five-Year Breast Cancer Risk Assessment
A new image-only AI model more accurately predicts five-year breast cancer risk than breast density alone, according to multinational research presented at RSNA 2025.
•Radiology Business
AI Model Uses CT Scans to Reveal Biomarker for Chronic Stress
Researchers developed an AI model to measure chronic stress using adrenal gland volume on routine CT scans.