Chen Wei-Pin, Teng Wei-Guang, Kuo C Benson, Yen Yu-Jui, Lian Jian-Yu, Sing Matthew, Chen Peng-Ting
Department of Biomedical Engineering, National Cheng Kung University, No.138, Shengli Rd, North District, Tainan, 701, Taiwan, 886 2757575 ext 63438.
Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan.
JMIR Med Inform. 2025 Jul 11;13:e67552. doi: 10.2196/67552.
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.
人工智能/机器学习（AI/ML）已经彻底改变了医疗保健行业，尤其是在医疗设备的开发和使用方面。美国食品药品监督管理局（FDA）已批准了878多种启用AI/ML的医疗设备，这反映出数量和应用范围都在不断增长的趋势。了解这些设备在违反FDA法规方面存在的独特挑战对于有效避免召回至关重要。这对于医疗设备的主动措施尤为相关。
本研究探讨AI/ML对医疗设备召回的影响，重点关注与启用AI/ML的设备相比其他设备类型相关的不同原因。从openFDA获取了与510（k）批准的设备相关的召回信息。建立了三个召回队列：“所有510（k）设备召回”、“软件相关设备召回”和“AI/ML设备召回”。
从openFDA获取510（k）批准的设备的召回信息。根据FDA清单识别启用AI/ML的医疗设备。建立了三个队列：“所有510（k）设备召回”、“软件相关设备召回”和“AI/ML设备召回”。对每个召回事件进行根本原因分析。
结果表明，虽然3个对照组中排名前5的召回根本原因相对相似，但比例有所不同，AI/ML设备在所有召回中占比87%，影响更大。与设计和开发相关的因素在AI/ML设备召回中起重要作用，与设备设计和软件设计相关的根本原因占召回的50%，强调了在开发过程中进行全面规划、纳入用户反馈和验证以降低召回概率的重要性。此外，软件的变化，包括设计变更和控制变更，也对AI/ML设备的召回有很大影响。
总之，本研究为与启用AI/ML的医疗设备召回相关的独特挑战和考虑因素提供了有价值的见解，为制造商在这一快速发展的技术领域加强验证计划和降低风险提供了指导。
