乳腺癌是全球重要的健康负担，全球的发病率和死亡率正在增加。早期筛查和准确的诊断对于改善预后至关重要。放射影像学技术如数字乳房X线摄影（DM）、数字乳房断层摄影（DBT）、磁共振成像（MRI）、超声（US）和核医学技术常用于乳腺癌评估。组织病理学（HP）作为确认恶性肿瘤的金标准。人工智能（AI）技术在定量表示医学影像方面显示出巨大潜力，可以有效地在乳腺癌的分割、诊断和预后中提供帮助。在本综述中，我们总结了乳腺癌AI技术的最新进展，包括1）通过数据增强改善图像质量，2）快速检测和分割乳腺病变以及诊断恶性肿瘤，3）通过基于AI的分类技术对癌症进行生物学特征描述，如分期和亚型分析，4）整合多组学数据预测临床结果，如转移、治疗反应和生存。然后，我们总结了可用于训练稳健、具有普遍性和可重复性的深度学习模型的大规模数据库。此外，我们对AI在现实世界应用中面临的挑战进行了总结，包括数据聚集、模型解释性和实践规范。此外，我们期待AI的临床实施将为个体化管理提供重要的指导。版权所有 © 2023 Elsevier Ltd. 保留所有权利。

Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.Copyright © 2023 Elsevier Ltd. All rights reserved.