{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T16:03:38Z","timestamp":1793030618676,"version":"4.5.0"},"reference-count":46,"publisher":"American Medical Association (AMA)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Intern Med"],"published-print":{"date-parts":[[2022,12,1]]},"abstract":"<jats:sec id=\"ab-isc220002-1\">\n                    <jats:title>Importance<\/jats:title>\n                    <jats:p>Contemporary approaches to artificial intelligence (AI) based on deep learning have generated interest in the application of AI to breast cancer screening (BCS). The US Food and Drug Administration (FDA) has approved several next-generation AI products indicated for BCS in recent years; however, questions regarding their accuracy, appropriate use, and clinical utility remain.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"ab-isc220002-2\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To describe the current FDA 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 the current system.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"ab-isc220002-3\">\n                    <jats:title>Evidence Review<\/jats:title>\n                    <jats:p>Premarket notifications and other publicly available documents used for FDA clearance and approval of AI products indicated for BCS from January 1, 2017, to December 31, 2021.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"ab-isc220002-4\">\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>Nine AI products indicated for BCS for identification of suggestive lesions and mammogram triage were included. Most of the products had been cleared through the 510(k) pathway, and all clearances were based on previously collected retrospective data; 6 products used multicenter designs; 7 products used enriched data; and 4 lacked details on whether products were externally validated. Test performance measures, including sensitivity, specificity, and area under the curve, were the main outcomes reported. Most of the devices used tissue biopsy as the criterion standard for BCS accuracy evaluation. Other clinical outcome measures, including cancer stage at diagnosis and interval cancer detection, were not reported for any of the devices.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"ab-isc220002-5\">\n                    <jats:title>Conclusions and Relevance<\/jats:title>\n                    <jats:p>The findings of this review suggest important gaps in reporting of data sources, data set type, validation approach, and clinical utility assessment. As AI-assisted reading becomes more widespread in BCS and other radiologic examinations, strengthened FDA evidentiary regulatory standards, development of postmarketing surveillance, a focus on clinically meaningful outcomes, and stakeholder engagement will be critical for ensuring the safety and efficacy of these products.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1001\/jamainternmed.2022.4969","type":"journal-article","created":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T11:02:12Z","timestamp":1667818932000},"page":"1306","source":"Crossref","is-referenced-by-count":85,"title":["Artificial Intelligence in Breast Cancer Screening"],"prefix":"10.1001","volume":"182","author":[{"given":"Kunal C.","family":"Potnis","sequence":"first","affiliation":[{"name":"Yale School of Medicine, New Haven, Connecticut"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph S.","family":"Ross","sequence":"additional","affiliation":[{"name":"Section of General 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