{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T02:05:15Z","timestamp":1781834715776,"version":"3.54.5"},"reference-count":26,"publisher":"SAGE Publications","issue":"11","license":[{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Acta Radiol"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The use of artificial intelligence (AI) in screen-reading of mammograms has shown promising results for cancer detection. However, less attention has been paid to the false positives generated by AI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To investigate mammographic features in screening mammograms with high AI scores but a true-negative screening result.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Material and Methods<\/jats:title>\n                    <jats:p>In this retrospective study, 54,662 screening examinations from BreastScreen Norway 2010\u20132022 were analyzed with a commercially available AI system (Transpara v. 2.0.0). An AI score of 1\u201310 indicated the suspiciousness of malignancy. We selected examinations with an AI score of 10, with a true-negative screening result, followed by two consecutive true-negative screening examinations. Of the 2,124 examinations matching these criteria, 382 random examinations underwent blinded consensus review by three experienced breast radiologists. The examinations were classified according to mammographic features, radiologist interpretation score (1\u20135), and mammographic breast density (BI-RADS 5th ed. a\u2013d).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The reviews classified 91.1% (348\/382) of the examinations as negative (interpretation score 1). All examinations (26\/26) categorized as BI-RADS d were given an interpretation score of 1. Classification of mammographic features: asymmetry = 30.6% (117\/382); calcifications = 30.1% (115\/382); asymmetry with calcifications = 29.3% (112\/382); mass = 8.9% (34\/382); distortion = 0.8% (3\/382); spiculated mass = 0.3% (1\/382). For examinations with calcifications, 79.1% (91\/115) were classified with benign morphology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The majority of false-positive screening examinations generated by AI were classified as non-suspicious in a retrospective blinded consensus review and would likely not have been recalled for further assessment in a real screening setting using AI as a decision support.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/02841851251363697","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T18:18:26Z","timestamp":1758046706000},"page":"1225-1232","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Mammographic features in screening mammograms with high AI scores but a true-negative screening result"],"prefix":"10.1177","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1884-3852","authenticated-orcid":false,"given":"Henrik Wethe","family":"Koch","sequence":"first","affiliation":[{"name":"Stavanger University Hospital"},{"name":"University of Stavanger"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3835-5880","authenticated-orcid":false,"given":"Marie Burns","family":"Bergan","sequence":"additional","affiliation":[{"name":"Department of Breast Screening, Cancer Registry, Norwegian Institute of Public Health, Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6756-3566","authenticated-orcid":false,"given":"Jonas","family":"Gjesvik","sequence":"additional","affiliation":[{"name":"Department of Breast Screening, Cancer Registry, Norwegian Institute of Public Health, Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5437-0363","authenticated-orcid":false,"given":"Marthe","family":"Larsen","sequence":"additional","affiliation":[{"name":"Department of Breast Screening, Cancer Registry, Norwegian Institute of Public Health, Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1486-4128","authenticated-orcid":false,"given":"Hauke","family":"Bartsch","sequence":"additional","affiliation":[{"name":"Haukeland University Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9313-7564","authenticated-orcid":false,"given":"Ingfrid Helene Salvesen","family":"Haldorsen","sequence":"additional","affiliation":[{"name":"Haukeland University Hospital"},{"name":"University of Bergen"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0178-8939","authenticated-orcid":false,"given":"Solveig","family":"Hofvind","sequence":"additional","affiliation":[{"name":"Department of Breast Screening, Cancer Registry, Norwegian Institute of Public Health, Oslo, Norway"},{"name":"UiT the Arctic University of Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21834"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/bjc.2013.177"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMsr1504363"},{"key":"e_1_3_2_5_2","unstructured":"International Agency for Research on Cancer. 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Breast cancer screening in women with extremely dense breasts recommendations of the European Society of Breast Imaging (EUSOBI). Eur Radiol 2022;32:4036\u20134045.","DOI":"10.1007\/s00330-022-08617-6"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1093\/bjr\/tqad024"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20230210"},{"key":"e_1_3_2_12_2","doi-asserted-by":"crossref","unstructured":"L\u00e5ng K Josefsson V Larsson AM et al. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised controlled non-inferiority single-blinded screening accuracy study. Lancet Oncol 2023;24:936\u2013944.","DOI":"10.1016\/S1470-2045(23)00298-X"},{"key":"e_1_3_2_13_2","doi-asserted-by":"crossref","unstructured":"Dembrower K Crippa A Colon E et al. 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