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Comparison of AI-integrated pathways with human-AI interaction in population mammographic screening for breast cancer

Author

Listed:
  • Helen M. L. Frazer

    (St Vincent’s Hospital Melbourne
    BreastScreen Victoria
    University of Melbourne)

  • Carlos A. Peña-Solorzano

    (St Vincent’s Institute of Medical Research
    University of Melbourne)

  • Chun Fung Kwok

    (St Vincent’s Institute of Medical Research
    University of Melbourne)

  • Michael S. Elliott

    (St Vincent’s Institute of Medical Research
    University of Melbourne)

  • Yuanhong Chen

    (University of Adelaide)

  • Chong Wang

    (University of Adelaide)

  • Jocelyn F. Lippey

    (St Vincent’s Hospital Melbourne
    St Vincent’s Hospital Melbourne
    University of Melbourne)

  • John L. Hopper

    (University of Melbourne)

  • Peter Brotchie

    (St Vincent’s Hospital Melbourne)

  • Gustavo Carneiro

    (University of Adelaide
    The University of Surrey)

  • Davis J. McCarthy

    (St Vincent’s Institute of Medical Research
    University of Melbourne)

Abstract

Artificial intelligence (AI) readers of mammograms compare favourably to individual radiologists in detecting breast cancer. However, AI readers cannot perform at the level of multi-reader systems used by screening programs in countries such as Australia, Sweden, and the UK. Therefore, implementation demands human-AI collaboration. Here, we use a large, high-quality retrospective mammography dataset from Victoria, Australia to conduct detailed simulations of five potential AI-integrated screening pathways, and examine human-AI interaction effects to explore automation bias. Operating an AI reader as a second reader or as a high confidence filter improves current screening outcomes by 1.9–2.5% in sensitivity and up to 0.6% in specificity, achieving 4.6–10.9% reduction in assessments and 48–80.7% reduction in human reads. Automation bias degrades performance in multi-reader settings but improves it for single-readers. This study provides insight into feasible approaches for AI-integrated screening pathways and prospective studies necessary prior to clinical adoption.

Suggested Citation

  • Helen M. L. Frazer & Carlos A. Peña-Solorzano & Chun Fung Kwok & Michael S. Elliott & Yuanhong Chen & Chong Wang & Jocelyn F. Lippey & John L. Hopper & Peter Brotchie & Gustavo Carneiro & Davis J. McC, 2024. "Comparison of AI-integrated pathways with human-AI interaction in population mammographic screening for breast cancer," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51725-8
    DOI: 10.1038/s41467-024-51725-8
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    References listed on IDEAS

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    1. Scott Mayer McKinney & Marcin Sieniek & Varun Godbole & Jonathan Godwin & Natasha Antropova & Hutan Ashrafian & Trevor Back & Mary Chesus & Greg S. Corrado & Ara Darzi & Mozziyar Etemadi & Florencia G, 2020. "International evaluation of an AI system for breast cancer screening," Nature, Nature, vol. 577(7788), pages 89-94, January.
    2. Quinn McNemar, 1947. "Note on the sampling error of the difference between correlated proportions or percentages," Psychometrika, Springer;The Psychometric Society, vol. 12(2), pages 153-157, June.
    3. Scott Mayer McKinney & Marcin Sieniek & Varun Godbole & Jonathan Godwin & Natasha Antropova & Hutan Ashrafian & Trevor Back & Mary Chesus & Greg S. Corrado & Ara Darzi & Mozziyar Etemadi & Florencia G, 2020. "Addendum: International evaluation of an AI system for breast cancer screening," Nature, Nature, vol. 586(7829), pages 19-19, October.
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