Tada et al. utilized a deep-learning mechanism to identify and diagnose melanocytic atypia. A dataset of 122 slides, stained immunohistochemically, was utilized to train the machine learning model; however, only around 37% of the slides were able to be used in the model. Even then, the deep-learning model exhibited highly productive results with staining for MelanA, MelPro, and SOX10 antibodies. The results of the different antibodies constituted a holistic picture, which was properly integrated by the neural network models. The models utilized in this study effectively aid in analyzing and diagnosing melanocytic atypia. However, Tada et al. suggest that in future studies, a stain that can distinguish between eccrine glands and melanocytes should be utilized, as eccrine glands caused slight difficulties with the deep-learning models.