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

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

The following numbered list outlines the processes used to collect, filter, and analyze data to derive insights into the prevalence and nature of recalls within each group (refer to Figure 3): All 510(k) devices recall: the “All 510(k) Devices Recall” cohort begins by gathering all 510(k) medical device recall events from the open FDA database [11]. We specifically exclude recalls related to AI/ML devices and De Novo devices to focus on traditional 510(k) recalls.

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

JMIR Med Inform 2025;13:e67552

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Correction: “Impact of Virtual Care With Remote Automated Monitoring on the Rate of Acute Hospital Care Post Discharge and Index Length of Hospital Stay: Protocol for the Post Discharge After Surgery Virtual Care With Remote Automated Monitoring Technology 3 (PVC-RAM-3) Trial”

Correction: “Impact of Virtual Care With Remote Automated Monitoring on the Rate of Acute Hospital Care Post Discharge and Index Length of Hospital Stay: Protocol for the Post Discharge After Surgery Virtual Care With Remote Automated Monitoring Technology 3 (PVC-RAM-3) Trial”

Sandra Ofori, Michael H McGillion, Flavia K Borges, Carley Ouellette, Ameen Patel, David Conen, Maura Marcucci, Michael Ke Wang, Lily Jaeyoung Park, Conor Bell, Jennifer Lounsbury, Kanae Nagatani, Vikas Tandon, Trevor J Wilkieson, Ahraaz Wyne, Valerie Harvey, Stephanie Harrison, Rahima Nenshi, Jessica Bogach, John Harlock, Margherita Cadeddu, Shawn Forbes, Shariq Haider, Reza D Mirza, Sunita Narang, Clare J Reade, Daniel M Tushinski, Amit Raut, Samir Raza, Ted Scott, Anthony Adili, Jeremy Petch, PJ Devereaux

J Med Internet Res 2025;27:e78893

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