The Path to Implementation of Artificial Intelligence in Screening Mammography Is Not All That Clear.

Breast cancer is the most common non–skin-related cancer among women and the leading cause of cancer-related deaths among women worldwide. While mammography screening has been shown to reduce breast cancer morbidity and mortality, the intrinsic limitation of a mammogram, namely, it being a 2-dimensional projection of a 3-dimensional structure, increases the complexity of the cancer detection task. In the United States, a single radiologist interprets screening mammograms to carry out this task, whereas most of Europe and parts of Asia use 2 radiologists (double reading). In the single-radiologist reading scenario, false-negative rates range between 10% and 30%, 1 and false-positive rates are such that 49% of women screened annually for 10 years will experience at least 1 false-positive mammogram result. 2 Clearly, this is a task that can benefit from artificial intelligence (AI), and indeed AI applications in breast cancer compose 12% of the total applications of AI in radiology. 3 In particular, deep learning applications in digital mammograms are on the rise. 4-6