Artificial intelligence for breast cancer screening in mammography (AI-STREAM): preliminary analysis of a prospective multicenter cohort study

Yun-Woo Chang (), Jung Kyu Ryu, Jin Kyung An, Nami Choi, Young Mi Park, Kyung Hee Ko and Kyunghwa Han
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Yun-Woo Chang: Soonchunhyang University Seoul Hospital
Jung Kyu Ryu: Kyung Hee University Hospital at Gangdong
Jin Kyung An: Nowon Eulgi University Hospital
Nami Choi: Konkuk University Medical center
Young Mi Park: Inje University Busan Paik Hospital
Kyung Hee Ko: CHA Bundang Medical center
Kyunghwa Han: Yonsei University College of Medicine

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract Artificial intelligence (AI) improves the accuracy of mammography screening, but prospective evidence, particularly in a single-read setting, remains limited. This study compares the diagnostic accuracy of breast radiologists with and without AI-based computer-aided detection (AI-CAD) for screening mammograms in a real-world, single-read setting. A prospective multicenter cohort study is conducted within South Korea’s national breast cancer screening program for women. The primary outcomes are screen-detected breast cancer within one year, with a focus on cancer detection rates (CDRs) and recall rates (RRs) of radiologists. A total of 24,543 women are included in the final cohort, with 140 (0.57%) screen-detected breast cancers. The CDR is significantly higher by 13.8% for breast radiologists using AI-CAD (n = 140 [5.70‰]) compared to those without AI (n = 123 [5.01‰]; p

Date: 2025
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DOI: 10.1038/s41467-025-57469-3

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