Comprehensive Summary
The study evaluates the cost-effectiveness of integrating an AI-based risk-stratified breast cancer screening model, Mirai, into the UK National Breast Cancer Screening Program. Researchers used a decision analytical AI model to simulate outcomes for women aged 50-70 years and adjusted screening intervals based on individual risk scores (ranging from annual to every six years). Results indicated the AI-guided regimen reduced costs while improving health outcomes. In fact, the most optimal regimen showed net monetary benefits of £60.4 to £85.3 million annually, with a reduction in screening frequency and an increase in quality-adjusted life-years (QALYs).
Outcomes and Implications
The research underscores the potential for AI in tailoring breast cancer screening strategies, enhancing both cost-efficiency and clinical outcomes. By personalizing intervals, the method reduces unnecessary screenings and allocates resources to high-risk groups. Not to mention, the findings are especially relevant for health systems with constrained budgets, suggesting that AI-guided models could address screening backlogs and improve early cancer detection. While these findings are promising currently, future prospective studies are necessary for the sake of validity and to facilitate clinical implementation.