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

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 and Davis J. McCarthy
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Helen M. L. Frazer: St Vincent’s Hospital Melbourne
Carlos A. Peña-Solorzano: St Vincent’s Institute of Medical Research
Chun Fung Kwok: St Vincent’s Institute of Medical Research
Michael S. Elliott: St Vincent’s Institute of Medical Research
Yuanhong Chen: University of Adelaide
Chong Wang: University of Adelaide
Jocelyn F. Lippey: St Vincent’s Hospital Melbourne
John L. Hopper: University of Melbourne
Peter Brotchie: St Vincent’s Hospital Melbourne
Gustavo Carneiro: University of Adelaide
Davis J. McCarthy: St Vincent’s Institute of Medical Research

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-51725-8

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