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Artificial intelligence for risk-stratified breast cancer screening: a systematic review of evidence, clinical integration, and ethical implications in risk assessment tools
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DOI：10.1007/s11547-026-02217-w.png)
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Abstract
En 中文
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. 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. 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. 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. 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.
Keywords:
Breast neoplasms
Mammography
Artificial intelligence
Machine learning
Deep learning
Risk assessment
Precision medicine
Mass screening
Risk-stratified cancer screening
Ethics
Medical
Health equity
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206
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