Artificial intelligence is becoming increasingly important in dermatology, with studies reporting accuracy matching or exceeding dermatologists for the diagnosis of skin lesions from clinical and dermoscopic images. However, real-world clinical validation is currently lacking. We review dermatological applications of deep learning, the leading artificial intelligence technology for image analysis, and discuss its current capabilities, potential failure modes, and challenges surrounding performance assessment and interpretability. We address the following three primary applications: (i) teledermatology, including triage for referral to dermatologists; (ii) augmenting clinical assessment during face-to-face visits; and (iii) dermatopathology. We discuss equity and ethical issues related to future clinical adoption and recommend specific standardization of metrics for reporting model performance.

人工智能在皮肤病学中变得越来越重要，据研究报告，从临床和皮肤镜图像诊断皮肤病变的准确性与皮肤科医生相匹配或超过皮肤科医生。但是，目前缺乏实际的临床验证。我们回顾了深度学习的皮肤病学应用程序（用于图像分析的领先人工智能技术），并讨论了其当前功能，潜在的故障模式以及性能评估和可解释性方面的挑战。我们处理以下三个主要应用：（i）远程皮肤病学，包括分诊给皮肤科医生的分流；（ii）在面对面访问期间增加临床评估；（iii）皮肤病理学。