Bioinformatics Seminar
Time: 11AM
Venue: Davis Auditorium and Online
6 August 2024
Comparison of AI-integrated pathways with human-AI interaction in population mammographic screening for breast cancer
Chun Fung (Jackson) KwokSt. Vincent's Institute of Medical Research
I’ll talk about how well Artificial Intelligence (AI) readers of mammograms compare to individual radiologists in detecting breast cancer, and how human-AI collaboration can match or even outperform the multi-reader systems used by screening programs in countries such as Australia, Sweden, and the UK. Our study uses a large, high-quality retrospective mammography dataset from Victoria, Australia to conduct detailed simulations of five potential AI-integrated screening pathways and examines 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.