Regulatory Insights From 27 Years of Artificial Intelligence/Machine Learning-Enabled Medical Device Recalls in the United States: Implications for Future Governance.

Authors

Wei-Pin Chen, Wei-Guang Teng, C Benson Kuo, Yu-Jui Yen, Jian-Yu Lian, Matthew Sing, Peng-Ting Chen

Published in

JMIR medical informatics. Volume 13. Pages e67552. Jul 11, 2025. Epub Jul 11, 2025.

Abstract

Artificial intelligence/machine learning (AI/ML) has revolutionized the health care industry, particularly in the development and use of medical devices. The US Food and Drug Administration (FDA) has authorized over 878 AI/ML-enabled medical devices, reflecting a growing trend in both quantity and application scope. Understanding the distinct challenges these devices present in terms of FDA regulation violations is crucial for effectively avoiding recalls. This is particularly pertinent for proactive measures regarding medical devices.
This study explores the impact of AI/ML on medical device recalls, focusing on the distinct causes associated with AI/ML-enabled devices compared with other device types. Recall information associated with 510(k)-cleared devices was obtained from openFDA. Three recall cohorts were established: "All 510(k) devices recall," "software-related devices recall," and "AI/ML devices recall."
Recall information for 510(k)-cleared devices was obtained from openFDA. AI/ML-enabled medical devices were identified based on FDA listings. Three cohorts were established: "All 510(k) devices recall," "software-related devices recall," and "AI/ML devices recall." Root cause analysis was conducted for each recall event.
The results indicate that while the top 5 recall root causes are relatively similar across the 3 control groups, the proportions vary, with AI/ML devices showing a higher impact for 87% of all recalls. Design and development-related factors play a significant role in recalls of AI/ML devices with root causes related to device design and software design accounting for 50% of recalls, emphasizing the importance of thorough planning, user feedback incorporation, and validation during the development process to reduce the probability of recalls. In addition, changes in software, including design changes and control changes, also contribute substantially to recalls in AI/ML devices.
In conclusion, this study provides valuable insights into the unique challenges and considerations associated with AI/ML-enabled medical device recalls, offering guidance for manufacturers to enhance verification plans and mitigate risks in this rapidly evolving technological landscape.

PMID:
40644609
Bibliographic data and abstract were imported from PubMed on 12 Jul 2025.

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