Objective:
To analyze the inclusion of randomized clinical trial data in FDA-cleared artificial intelligence medical devices and assess their benefit-risk reporting.
Approach:
- Study Design: Cross-sectional analysis of AI and machine learning-enabled medical devices cleared by the FDA from September 1995 to July 2023.
- Data Sources: Utilized FDA decision summaries, device approvals databases, and Manufacturer and User Facility Device Experience and Medical Device Recalls databases.
Key Findings:
- Fewer than 2% of devices included randomized clinical trial data.
- 691 FDA-cleared AI/machine learning devices identified, primarily class II and cleared via the 510(k) pathway.
- Most devices were in radiology (76.9%), followed by cardiovascular medicine (10.1%) and neurology (2.9%).
- Study design was not described for 46.7% of devices, and sample size was missing for 53.3%.
- Only 6 devices (1.6%) cited randomized clinical trial data; 53 (7.7%) included prospective studies.
- Clinical performance outcomes were rarely reported, with sensitivity provided for 24% (166 devices) and specificity for 22% (152 devices).
- Demographic characteristics were disclosed in 4.5% (31 device summaries).
- Postmarket surveillance revealed 489 adverse events associated with 36 devices, including 1 death.
Interpretation:
Limitations:
- Limited reporting on study design and sample sizes for a significant proportion of devices.
- Low percentage of devices reporting clinical performance outcomes.
Conclusion:
Sources:
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