Screening mammography aims to identify breast cancer before symptoms appear, enabling earlier therapy for more treatable disease. Despite the existence of screening programs worldwide, interpretation of these images suffers from suboptimal rates of false positives and false negatives. Here we present an AI system capable of surpassing a single expert reader in breast cancer prediction performance. Using two large data sets representative of clinical practice in the United States (US) and the United Kingdom (UK), we show an absolute reduction of 5.7%/1.2% We show evidence of the system's ability to generalize from the UK sites to the US site.