Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful¹. Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives². Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening.

乳房 X 线摄影筛查的目的是在疾病的早期阶段识别乳腺癌，此时治疗可以更成功¹。尽管全世界都存在筛查计划，但乳房 X 线照片的解释受到高假阳性率和假阴性率的影响². 在这里，我们展示了一种人工智能 (AI) 系统，该系统能够在乳腺癌预测方面超越人类专家。为了评估其在临床环境中的表现，我们策划了来自英国的大型代表性数据集和来自美国的大型丰富数据集。我们显示，假阳性绝对减少了 5.7% 和 1.2%（美国和英国），假阴性绝对减少了 9.4% 和 2.7%。我们提供了系统从英国推广到美国的能力的证据。在对六位放射科医师的独立研究中，AI 系统的表现优于所有人类读者：AI 系统的接收器操作特征曲线下面积 (AUC-ROC) 绝对大于普通放射科医师的 AUC-ROC 11.5%。我们进行了模拟，其中人工智能系统参与了英国使用的双读过程，发现人工智能系统保持了非劣质性能，并将第二读卡器的工作量减少了 88%。这种对 AI 系统的强大评估为临床试验铺平了道路，以提高乳腺癌筛查的准确性和效率。