International evaluation of an AI system for breast cancer screening

Scott Mayer McKinney
Varun Yatindra Godbole
Jonathan Godwin
Natasha Antropova
Hutan Ashrafian
Trevor John Back
Mary Chesus
Ara Darzi
Mozziyar Etemadi
Florencia Garcia-Vicente
Fiona J Gilbert
Mark D Halling-Brown
Demis Hassabis
Sunny Jansen
Dominic King
David Melnick
Hormuz Mostofi
Lily Hao Yi Peng
Joshua Reicher
Bernardino Romera Paredes
Richard Sidebottom
Mustafa Suleyman
Kenneth C. Young
Jeffrey De Fauw
Shravya Ramesh Shetty


Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful. Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives. 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.