1. Comparison of AI-integrated pathways with human-AI interaction in population mammographic screening for breast cancer.
- Author
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Frazer, Helen M. L., Peña-Solorzano, Carlos A., Kwok, Chun Fung, Elliott, Michael S., Chen, Yuanhong, Wang, Chong, Al-Qershi, Osamah, Fox, Samantha K., Hill, Brendan, Karthik, Ravishankar, Kunicki, Katrina, Li, Shuai, Makalic, Enes, Nguyen, Tuong L., Ralalage, Prabhathi Basnayake, Schmidt, Daniel, Weideman, Prue C., Lippey, Jocelyn F., Hopper, John L., and Brotchie, Peter
- Subjects
ARTIFICIAL intelligence ,MEDICAL screening ,EARLY detection of cancer ,BREAST cancer ,MAMMOGRAMS - 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. Successful human-AI collaboration could greatly contribute to breast cancer mammographic screening. Here, the authors use a large-scale retrospective mammography dataset to simulate and compare five plausible AI-integrated screening pathways, finding optimal ways in which human-AI collaboration could be implemented in real-world settings. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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