Bejnordi BE, et al. - This study was initiated in order to gauge the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer. Researchers also comparatively analyzed it with pathologists’ diagnoses in a diagnostic setting. A better diagnostic performance was yielded by some deep learning algorithms in the setting of a challenge competition when compared to a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow. The results displayed comparable algorithm performance with an expert pathologist interpreting whole-slide images without time constraints. In order to ascertain if this approach had clinical utility, advanced research was warranted.