@article{11705,
  author = {Scott Mayer McKinney and Marcin Sieniek and Varun Godbole and Jonathan Godwin and Natasha Antropova and Hutan Ashrafian and Trevor Back and Mary Chesus and Greg C. Corrado and Ara Darzi and Mozziyar Etemadi and Florencia Garcia-Vicente and Fiona J. Gilbert and Mark Halling-Brown and Demis Hassabis and Sunny Jansen and Alan Karthikesalingam and Christopher J. Kelly and Dominic King and Joseph R. Ledsam and David Melnick and Hormuz Mostofi and Lily Peng and Joshua Jay Reicher and Bernardino Romera-Paredes and Richard Sidebottom and Mustafa Suleyman and Daniel Tse and Kenneth C. Young and Jeffrey De Fauw and Shravya Shetty},
  title = {International evaluation of an AI system for breast cancer screening.},
  abstract = {<p>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.</p>},
  year = {2020},
  journal = {Nature},
  volume = {577},
  pages = {89-94},
  month = {12/2020},
  issn = {1476-4687},
  doi = {10.1038/s41586-019-1799-6},
  language = {eng},
}