Artificial intelligence for risk-stratified breast cancer screening: a systematic review of evidence, clinical integration, and ethical implications in risk assessment tools.

BACKGROUND

Conventional age-based breast cancer screening ignores substantial inter-individual risk variation, contributing to overdiagnosis, false positives, and missed opportunities for earlier detection in high-risk women. Mammography-based artificial intelligence (AI) may enable risk-stratified screening and more efficient workflows.

PURPOSE: To systematically review evidence on mammography-based AI for personalized breast cancer screening, covering risk prediction, detection/triage, decision support, and associated ethical, economic, and equity implications.

METHODS

We searched MEDLINE/PubMed, Embase, Scopus, Web of Science, and the Cochrane Library (January 2015-November 2025) for studies evaluating AI-enabled personalization in breast cancer screening. Two reviewers independently screened 612 records, assessed 77 full texts, and included 30 studies; data were synthesized narratively.

RESULTS

Image-based deep-learning risk models consistently outperformed traditional clinical risk tools and enriched future cancers within small high-risk strata, including cancers presenting as interval cancers in recent validations. Prospective trials and real-world implementations indicate that AI-supported reading can maintain or modestly improve cancer detection while reducing radiologist workload by roughly 40-50%. Decision-analytic models suggest that AI-enabled risk-stratified policies may be cost-effective but rely on assumptions and lack prospective confirmation of long-term endpoints. Key evidence gaps include prospective demonstration that acting on AI-derived risk reduces interval/advanced cancers, subgroup equity performance, explainability, and governance.

CONCLUSIONS

AI is a credible enabler of personalized mammography screening, with the most mature near-term use cases being identification of women at highest short-term risk for intensified surveillance and workflow optimization via AI-supported reading. Interval extension for low-risk groups should be implemented only within carefully monitored pilots and prospective outcome studies, with predefined safety, equity audits, and governance safeguards.