乳腺癌是全球最常见的恶性肿瘤，在2020年新增了超过2.26百万例。乳腺活检标本的组织学检查是诊断其病理类型的主要手段，但此方法存在劳动强度大、主观性强、易出错等问题。人工智能（AI）技术可以为乳腺活检的癌症检测和分类提供支持，确保快速、准确和客观的诊断。在这里，我们介绍了一种基于AI的质量控制解决方案，该质量控制解决方案的开发、外部临床验证和日常应用部署。该AI算法的基础是识别51种临床和形态特征，通过大规模的多中心验证研究，在肿瘤侵袭性癌和管内癌（DCIS）的检测方面取得了非常高的准确度。特别是对于侵袭性导管癌（IDC）和侵袭性小叶癌（ILC）之间的亚型区分，以及DCIS高级别与低级别/非典型导管增生的区分，其曲线下面积（AUC）分别为0.97和0.92，且对肿瘤间质浸润淋巴细胞（TILs）的鉴别也非常准确（AUC为0.965）。将该AI解决方案作为实时质量控制解决方案应用于临床例行工作中，可以发现初步被手工检查遗漏的癌症，展示了其在真实世界临床应用中的临床实用价值和准确性。 ©2022. 作者。

Breast cancer is the most common malignant disease worldwide, with over 2.26 million new cases in 2020. Its diagnosis is determined by a histological review of breast biopsy specimens, which can be labor-intensive, subjective, and error-prone. Artificial Intelligence (AI)-based tools can support cancer detection and classification in breast biopsies ensuring rapid, accurate, and objective diagnosis. We present here the development, external clinical validation, and deployment in routine use of an AI-based quality control solution for breast biopsy review. The underlying AI algorithm is trained to identify 51 different types of clinical and morphological features, and it achieves very high accuracy in a large, multi-site validation study. Specifically, the area under the receiver operating characteristic curves (AUC) for the detection of invasive carcinoma and of ductal carcinoma in situ (DCIS) are 0.99 (specificity and sensitivity of 93.57 and 95.51%, respectively) and 0.98 (specificity and sensitivity of 93.79 and 93.20% respectively), respectively. The AI algorithm differentiates well between subtypes of invasive and different grades of in situ carcinomas with an AUC of 0.97 for invasive ductal carcinoma (IDC) vs. invasive lobular carcinoma (ILC) and AUC of 0.92 for DCIS high grade vs. low grade/atypical ductal hyperplasia, respectively, as well as accurately identifies stromal tumor-infiltrating lymphocytes (TILs) with an AUC of 0.965. Deployment of this AI solution as a real-time quality control solution in clinical routine leads to the identification of cancers initially missed by the reviewing pathologist, demonstrating both clinical utility and accuracy in real-world clinical application.© 2022. The Author(s).