Conventional breast cancer screening programs are predominantly age-based, applying uniform intervals and modalities across broad populations. While this model has reduced mortality, it entails harms—including overdiagnosis, false positives, and missed interval cancers—prompting interest in risk-stratified approaches. In recent years, artificial intelligence (AI) has emerged as a critical enabler of this paradigm shift. This narrative review examines how AI-driven tools are advancing breast cancer screening toward personalization, with a focus on mammographic risk models, multimodal risk prediction, and AI-enabled clinical decision support. We reviewed studies published from 2015 to 2025, prioritizing large cohorts, randomized trials, and prospective validations. AI-based mammographic risk models generally improve discrimination versus classical models and are being externally validated; however, evidence remains heterogeneous across subtypes and populations. Emerging multimodal models integrate genetics, clinical data, and imaging; AI is also being evaluated for triage and personalized intervals within clinical workflows. Barriers remain—explainability, regulatory validation, and equity. Widespread adoption will depend on prospective clinical benefit, regulatory alignment, and careful integration. Overall, AI-based mammographic risk models generally improve discrimination versus classical models and are being externally validated; however, evidence remains heterogeneous across molecular subtypes, with signals strongest for ER-positive disease and limited data for fast-growing and interval cancers. Prospective trials demonstrating outcome benefit and safe interval modification are still pending. Accordingly, adoption should proceed with safeguards, equity monitoring, and clear separation between risk prediction, lesion detection, triage, and decision-support roles
传统的乳腺癌筛查项目主要基于年龄，在广泛人群中采用统一的筛查间隔和模式。虽然这种模式降低了死亡率，但也带来了过度诊断、假阳性以及间期癌漏诊等危害，这促使人们关注风险分层筛查方法。近年来，人工智能已成为推动这一范式转变的关键赋能技术。本文通过叙述性综述，探讨人工智能驱动工具如何推动乳腺癌筛查向个体化方向发展，重点关注乳腺X线摄影风险模型、多模态风险预测以及人工智能辅助的临床决策支持。我们回顾了2015年至2025年间发表的研究，优先纳入大型队列研究、随机试验和前瞻性验证研究。基于人工智能的乳腺X线摄影风险模型通常比传统模型具有更好的区分度，并正在接受外部验证；然而，在不同亚型和人群中的证据仍存在异质性。新兴的多模态模型整合了遗传学、临床数据和影像学信息；人工智能在临床工作流程中用于分诊和个体化筛查间隔的评估也正在进行中。可解释性、监管验证和公平性等障碍依然存在。广泛采用将取决于前瞻性临床获益、监管协调以及审慎的整合。总体而言，基于人工智能的乳腺X线摄影风险模型通常比传统模型具有更好的区分度，并正在接受外部验证；然而，在不同分子亚型中的证据仍存在差异，其中对ER阳性疾病的预测信号最强，而对快速生长型和间期癌的数据有限。能够证明改善临床结局和安全性调整筛查间隔的前瞻性试验仍有待进行。因此，在推广应用时需建立保障措施、公平性监测机制，并明确区分风险预测、病灶检测、分诊和决策支持的不同功能角色。
肿瘤(癌症)患者之家