Data Quality, Data Sharing, and Moving Artificial Intelligence Forward

Buda et al 1 have curated and annotated a data set of 3-dimensional digital breast tomosynthesis (DBT) examinations obtained from 5060 patients. In using this data set, they developed a deep learning algorithm for breast cancer detection and reached a sensitivity of 65% at 2 false positives per breast on a test set from 418 patients. Compared with the reported performance of several commercial artificial intelligence (AI) products for mammography, 2 the performance of their model is not great. However, tasking AI to detect breast cancer in DBT examinations, in comparison with 2-dimensional digital mammograms, remains notoriously challenging. The