Artificial Intelligence in Breast Cancer Screening: Evaluation of FDA Device Regulation and Future Recommendations.
Artificial Intelligence in Breast Cancer Screening: Evaluation of FDA Device Regulation and Future Recommendations.
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DOI：
10.1001/jamainternmed.2022.4969
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发表时间：
2022-12-01
影响因子：
39
通讯作者：
Richman, Ilana B.
中科院分区：
文献类型：
--
作者：
Potnis, Kunal C.;Ross, Joseph S.;Aneja, Sanjay;Gross, Cary P.;Richman, Ilana B.
Contemporary approaches to artificial intelligence (AI) based on deep learning have generated interest in the application of AI to breast cancer screening (BCS). The U.S. Food and Drug Administration (FDA) has approved several next-generation AI products with an indication for BCS in recent years. However, questions related to AI’s accuracy, appropriate use, and clinical utility remain. To describe FDA’s current regulatory process for AI products, summarize the evidence used to support FDA clearance and approval of AI products indicated for BCS, consider the advantages and limitations of current regulatory approaches, and suggest ways to improve on the current system. We reviewed premarket notifications and other publicly available FDA documents used in the clearance and approval of AI products with an indication for BCS from January 1, 2017 through December 31, 2021. Nine AI products indicated for BCS suspicious lesion identification and mammogram triage were included. The majority of products were cleared through the 510(k) pathway, and all clearances were based on previously collected, retrospective data. Six products used multicenter designs. Enriched data were used for eight devices, and four devices lacked details on whether products were externally validated. Test performance measures, including sensitivity, specificity, and area under the curve, were the main outcomes reported. The majority of devices used tissue biopsy as their gold standard for BCS accuracy evaluation. Other measures of clinical utility, including cancer stage at detection, interval cancer detection, or other outcomes, were not reported for any of the devices. Important gaps in reporting of data sources, dataset type, validation approach, and clinical utility assessment were identified. As AI-assisted reading becomes more widespread in BCS and other radiologic exams, strengthened FDA evidentiary regulatory standards, development of post-marketing surveillance, a focus on clinically meaningful outcomes, and stakeholder engagement of will be critical for ensuring safety and efficacy of these products.
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