{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T19:52:02Z","timestamp":1764100322149,"version":"3.46.0"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"ForTra gGmbH f\u00fcr Forschungsstransfer der Else Kr\u00f6ner-Fresenius-Stiftung"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Breast diffusion-weighted imaging (DWI) has shown potential as a standalone imaging technique for certain indications, eg, supplemental screening of women with dense breasts. This study evaluates an artificial intelligence (AI)-powered computer-aided diagnosis (CAD) system for clinical interpretation and workload reduction in breast DWI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>This retrospective IRB-approved study included: n\u2009=\u2009824 examinations for model development (2017-2020) and n\u2009=\u2009235 for evaluation (01\/2021-06\/2021). Readings were performed by three readers using either the AI-CAD or manual readings. BI-RADS-like (Breast Imaging Reporting and Data System) classification was based on DWI. Histopathology served as ground truth. The model was nnDetection-based, trained using 5-fold cross-validation and ensembling. Statistical significance was determined using McNemar\u2019s test. Inter-rater agreement was calculated using Cohen\u2019s kappa. Model performance was calculated using the area under the receiver operating curve (AUC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The AI-augmented approach significantly reduced BI-RADS-like 3 calls in breast DWI by 29% (P\u00a0=.019) and increased interrater agreement (0.57\u2009\u00b1\u20090.10 vs 0.49\u2009\u00b1\u20090.11), while preserving diagnostic accuracy. Two of the three readers detected more malignant lesions (63\/69 vs 59\/69 and 64\/69 vs 62\/69) with the AI-CAD. The AI model achieved an AUC of 0.78 (95% CI: [0.72, 0.85]; P\u00a0&amp;lt;.001), which increased for women at screening age to 0.82 (95% CI: [0.73, 0.90]; P\u00a0&amp;lt;.001), indicating a potential for workload reduction of 20.9% at 96% sensitivity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion and Conclusion<\/jats:title>\n                    <jats:p>Breast DWI might benefit from AI support. In our study, AI showed potential for reduction of BI-RADS-like 3 calls and increase of inter-rater agreement. However, given the limited study size, further research is needed.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocaf156","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T11:57:42Z","timestamp":1757505462000},"page":"1908-1915","source":"Crossref","is-referenced-by-count":0,"title":["Including AI in diffusion-weighted breast MRI has potential to increase reader confidence and reduce workload"],"prefix":"10.1093","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3361-1698","authenticated-orcid":false,"given":"Dimitrios","family":"Bounias","sequence":"first","affiliation":[{"name":"German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing , Heidelberg 69120,","place":["Germany"]},{"name":"Medical Faculty Heidelberg, Heidelberg University , Heidelberg 69120,","place":["Germany"]}]},{"given":"Lina","family":"Simons","sequence":"additional","affiliation":[{"name":"Institute of Radiology, 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