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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 ofthe ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all ofthe 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 ofthe 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.
Breast cancer is the second leading cause of death from cancer in women3, but early detection and treatment can considerably improve outcomes1,4,5. As a consequence, many developed nations have implemented large-scale mammography screening programmes. Major medical and governmental organizations recommend screening for all women starting between the ages of 40 and 506-8. In the USA and UK combined, over 42 million exams are performed each year9,10.
Despite the widespread adoption of mammography, interpretation of these images remains challenging. The accuracy achieved by experts in cancer detection varies widely, and the performance of even the best clinicians leaves room for improvement11,12. False positives can lead to patient anxiety13, unnecessary follow-up and invasive diagnostic procedures. Cancers that are missed at screening may not be identified until they are more advanced and less amenable to treatment14.
AI may be uniquely poised to help with this challenge. Studies have demonstrated the...