{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T17:46:54Z","timestamp":1782323214355,"version":"3.54.5"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T00:00:00Z","timestamp":1728864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R21 HS029257"],"award-info":[{"award-number":["R21 HS029257"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,1,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>There are limited data on the application of artificial intelligence (AI) on nonenriched, real-world screening mammograms. This work aims to evaluate the ability of AI to detect false negative cancers not detected at the time of screening when reviewed by the radiologist alone.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>A commercially available AI algorithm was retrospectively applied to patients undergoing screening full-field digital mammography (FFDM) or digital breast tomosynthesis (DBT) at a single institution from 2010 to 2019. Ground truth was established based on 1-year follow-up data. Descriptive statistics were performed with attention focused on AI detection of false negative cancers within these subsets.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>A total of 26\u2009694 FFDM and 3183 DBT examinations were analyzed. Artificial intelligence was able to detect 7\/13 false negative cancers (54%) in the FFDM cohort and 4\/10 (40%) in the DBT cohort on the preceding screening mammogram that was interpreted as negative by the radiologist. Of these, 4 in the FFDM cohort and 4 in the DBT cohort were identified in breast densities of C or greater. False negative cancers detected by AI were predominantly luminal A invasive malignancies (9\/11, 82%). Artificial intelligence was able to detect these false negative cancers a median time of 272 days sooner in the FFDM cohort and 248 days sooner in the DBT cohort compared to the radiologist.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>Artificial intelligence was able to detect cancers at the time of screening that were missed by the radiologist. Prospective studies are needed to evaluate the synergy of AI and the radiologist in real-world settings, especially on DBT examinations.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jbi\/wbae058","type":"journal-article","created":{"date-parts":[[2024,8,18]],"date-time":"2024-08-18T22:14:15Z","timestamp":1724019255000},"page":"16-26","source":"Crossref","is-referenced-by-count":7,"title":["External Validation of a Commercial Artificial Intelligence Algorithm on a Diverse Population for Detection of False Negative Breast Cancers"],"prefix":"10.1093","volume":"7","author":[{"given":"S Reed","family":"Plimpton","sequence":"first","affiliation":[{"name":"Department of Radiological Sciences, David Geffen School of Medicine at UCLA , Los Angeles, CA ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hannah","family":"Milch","sequence":"additional","affiliation":[{"name":"Department of Radiological Sciences, David Geffen School of Medicine at UCLA , Los Angeles, CA ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Sears","sequence":"additional","affiliation":[{"name":"Department of Radiological Sciences, David Geffen School of Medicine at UCLA , Los Angeles, CA ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6385-604X","authenticated-orcid":false,"given":"James","family":"Chalfant","sequence":"additional","affiliation":[{"name":"Department of Radiological Sciences, David Geffen School of Medicine at UCLA , Los Angeles, CA ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne","family":"Hoyt","sequence":"additional","affiliation":[{"name":"Department of Radiological Sciences, David Geffen School of Medicine at UCLA , Los Angeles, CA ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheryce","family":"Fischer","sequence":"additional","affiliation":[{"name":"Department of Radiological Sciences, David Geffen School of Medicine at UCLA , Los Angeles, CA ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Hsu","sequence":"additional","affiliation":[{"name":"Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA , Los Angeles, CA ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melissa","family":"Joines","sequence":"additional","affiliation":[{"name":"Department of Radiological Sciences, David Geffen School of Medicine at UCLA , Los Angeles, CA ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,10,14]]},"reference":[{"issue":"17","key":"2025012707411945600_CIT0001","doi-asserted-by":"publisher","first-page":"1784","DOI":"10.1056\/NEJMoa050518","article-title":"Cancer Intervention and Surveillance Modeling Network (CISNET) Collaborators. 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