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Millions of breast imaging exams are performed each year in an effort to reduce morbidity and mortality rates of breast cancer. Breast imaging exams are mainly performed for cancer screening, diagnostic workup of suspicious findings, evaluating extent of disease in recently diagnosed breast cancer patients, and determining treatment response. Yet, the interpretation of breast imaging can be subjective, tedious, time-consuming, and prone to human error. Retrospective and small reader studies suggest that deep learning (DL) has great potential to perform medical imaging tasks at or above human-level performance, and may be used to automate aspects of the breast cancer screening process, improve cancer detection rates, decrease unnecessary callbacks and biopsies, optimize patient risk assessment, and open up new possibilities for disease prognostication. Prospective trials are urgently needed to validate these proposed tools, and to evaluate whether they are ready for real-world clinical use. Additionally, new regulatory frameworks are needed to address the unique ethical, medicolegal, and quality control issues that DL algorithms present. In this article, we review the basics of DL, describe recent DL breast imaging applications including cancer detection and risk prediction, and finally conclude by discussing the challenges and future directions of artificial intelligence-based systems in the field of breast cancer.

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