Simple SummaryNon-melanoma skin cancer is one of the most common cancer diagnoses in the world, and cases are rising globally. Consequently, novel technology to aid in screening is of interest. Deep learning is one type of artificial intelligence that has shown promise in image analysis and is an attractive tool for application in non-melanoma skin cancer diagnosis. Here, we review the evidence for deep learning in non-melanoma skin cancer screening and diagnosis, the available technologies and remote applications, and attitudes and ethical considerations regarding such technologies.AbstractSkin cancer is the most common cancer diagnosis in the United States, with approximately one in five Americans expected to be diagnosed within their lifetime. Non-melanoma skin cancer is the most prevalent type of skin cancer, and as cases rise globally, physicians need reliable tools for early detection. Artificial intelligence has gained substantial interest as a decision support tool in medicine, particularly in image analysis, where deep learning has proven to be an effective tool. Because specialties such as dermatology rely primarily on visual diagnoses, deep learning could have many diagnostic applications, including the diagnosis of skin cancer. Furthermore, with the advancement of mobile smartphones and their increasingly powerful cameras, deep learning technology could also be utilized in remote skin cancer screening applications. Ultimately, the available data for the detection and diagnosis of skin cancer using deep learning technology are promising, revealing sensitivity and specificity that are not inferior to those of trained dermatologists. Work is still needed to increase the clinical use of AI-based tools, but based on the current data and the attitudes of patients and physicians, deep learning technology could be used effectively as a clinical decision-making tool in collaboration with physicians to improve diagnostic efficiency and accuracy.