人工智能用于风险分层乳腺癌筛查：证据、临床整合与伦理影响的系统综述
图像深度学习风险模型优于传统临床工具，AI辅助阅片可维持或轻度提高癌症检出并减少约40-50%放射科医生工作量；对低风险组延长筛查间隔须仅在严格监测的前瞻性研究中实施。
要点
- AI辅助阅片可维持或轻度提高癌症检出，同时减少约40-50%放射科医生工作量。
- 图像深度学习风险模型优于传统临床风险工具，可将未来癌症富集于小的高风险分层。
- 对低风险组延长筛查间隔须仅在严格监测的前瞻性研究中实施，并预设安全与公平性审计。
核心结论
- AI是个性化乳腺X线筛查的可信促进因素，近期最成熟用途为识别最高短期风险女性以加强监测和通过AI辅助阅片优化工作流程。
诊断标准与关键数值
- 图像深度学习风险模型持续优于传统临床风险工具，并在小的高风险分层中富集未来癌症，包括近期验证中表现为间期癌的癌症。
- 前瞻性试验和真实世界实施表明，AI辅助阅片可维持或轻度提高癌症检出，同时减少放射科医生工作量约40-50%。
- 决策分析模型提示AI驱动的风险分层策略可能具有成本效益，但依赖假设且缺乏长期终点的前瞻性确认。
临床应用与阅片要点
- 日常可优先将AI用于识别最高短期风险女性以加强监测，以及AI辅助阅片优化工作流程。
- 对低风险组延长筛查间隔须仅在严格监测的前瞻性研究中实施，并预设安全、公平性审计和治理保障。
局限性
- 关键证据缺口包括缺乏前瞻性证明依据AI风险采取行动可减少间期癌/晚期癌症、亚组公平性性能、可解释性和治理。
- 决策分析模型的成本效益结论依赖假设，缺乏长期终点的前瞻性确认。
以上解读依据论文摘要（全文未开放获取或许可不允许翻译转载）。
英文原文（摘要）· 点击展开
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.
引用
- Pesapane F, Caumo F, Mantellini P, et al. Artificial intelligence for risk-stratified breast cancer screening: a systematic review of evidence, clinical integration, and ethical implications in risk assessment tools. Radiol Med. 2026. PMID: 42149286. doi:10.1007/s11547-026-02217-w
供执业医师工作参考，不替代临床判断；数值与分级以指南原文为准。