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.

乳腺癌是一个重大的全球健康负担，全球发病率和死亡率不断上升。早期筛查和准确诊断对于改善预后至关重要。数字乳房X线摄影（DM）、数字乳房断层合成（DBT）、磁共振成像（MRI）、超声（US）和核医学技术等放射线成像模式通常用于乳腺癌评估。组织病理学（HP）是确认恶性肿瘤的金标准。人工智能（AI）技术在医学图像的定量表示方面显示出巨大的潜力，可以有效地协助乳腺癌的分割、诊断和预后。在这篇综述中，我们概述了乳腺癌人工智能技术的最新进展，包括 1) 通过数据增强提高图像质量，2) 乳腺病变的快速检测和分割以及恶性肿瘤的诊断，3) 癌症的生物学特征，例如分期以及基于人工智能的分类技术进行子分型，4）通过整合多组学数据来预测临床结果，例如转移、治疗反应和生存。然后，我们总结了可用于帮助训练稳健、可泛化和可重复的深度学习模型的大型数据库。此外，我们总结了人工智能在现实应用中面临的挑战，包括数据整理、模型可解释性和实践法规。此外，我们预计人工智能的临床实施将为患者的个性化管理提供重要指导。