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

Author

Listed:
  • 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
    Yonsei University College of Medicine)

  • Kyunghwa Han

    (Yonsei University College of Medicine)

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

Suggested Citation

  • Yun-Woo Chang & Jung Kyu Ryu & Jin Kyung An & Nami Choi & Young Mi Park & Kyung Hee Ko & Kyunghwa Han, 2025. "Artificial intelligence for breast cancer screening in mammography (AI-STREAM): preliminary analysis of a prospective multicenter cohort study," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57469-3
    DOI: 10.1038/s41467-025-57469-3
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