Skin cancer is one of the most prevalent and deadly forms of cancer worldwide, highlighting the critical importance of early detection and diagnosis in improving patient outcomes. Deep learning (DL) has shown significant promise in enhancing the accuracy and efficiency of automated skin disease diagnosis, particularly in detecting and classifying skin lesions. However, several challenges remain for DL-based skin cancer diagnosis, including complex features, image noise, intra-class variation, inter-class similarity, and data imbalance. This review synthesizes recent research and discusses innovative approaches to address these challenges, such as data augmentation, hybrid models, and feature fusion. Furthermore, the review highlights the integration of DL models into clinical workflows, offering insights into the potential of deep learning to revolutionize skin disease diagnosis and improve clinical decision-making. This review uniquely integrates a PRISMA-based methodology with a challenge-oriented taxonomy, providing a systematic and transparent synthesis of recent deep learning advances for skin disease diagnosis. It further highlights emerging directions such as hybrid CNN-Transformer architectures and uncertainty-aware models, emphasizing its contribution to future dermatological AI research.

翻译：皮肤癌是全球范围内最普遍且致命的癌症类型之一，凸显了早期检测与诊断对于改善患者预后的至关重要性。深度学习在提升自动化皮肤病诊断的准确性与效率方面展现出巨大潜力，尤其在皮肤病变的检测与分类领域。然而，基于深度学习的皮肤癌诊断仍面临诸多挑战，包括复杂特征、图像噪声、类内差异、类间相似性以及数据不平衡等。本综述综合了近期研究，并探讨了应对这些挑战的创新方法，如数据增强、混合模型和特征融合。此外，本文重点讨论了深度学习模型与临床工作流程的整合，深入剖析了深度学习在革新皮肤病诊断及改善临床决策方面的潜力。本综述独特地将基于PRISMA的方法论与面向挑战的分类体系相结合，为皮肤病诊断的近期深度学习进展提供了系统且透明的综合评述。文中进一步强调了新兴研究方向，如混合CNN-Transformer架构与不确定性感知模型，彰显了其对未来皮肤病学人工智能研究的贡献。