Research on Automatic Recognition and Auxiliary Diagnosis of Artificial Intelligence in Skin Diseases

Artificial intelligence (AI) has made significant strides in skin disease diagnosis, demonstrating impressive potential in the classification and detection of conditions such as skin cancer. However, several challenges remain that hinder the full clinical adoption of AI-driven diagnostic systems. This paper reviews the current advancements and key research areas aimed at optimizing AI models for dermatological applications. Key areas for future research include the development of more advanced deep learning architectures, such as multimodal fusion methods, which integrate dermoscopic images with structured clinical data for more comprehensive diagnostics. Additionally, there is a growing emphasis on creating personalized AI models that account for individual patient characteristics, such as age, gender, and genetics, to improve diagnostic accuracy. The integration of explainable AI (XAI) techniques, auxiliary tasks like lesion segmentation, and multi-center clinical research is essential to ensure transparency, trust, and generalizability across diverse populations. Moreover, ethical concerns related to bias, data privacy, and accountability must be addressed to ensure fairness and transparency in AI systems. This review highlights the importance of interdisciplinary collaboration and proposes future directions to enhance the reliability, scalability, and clinical applicability of AI-driven skin disease diagnosis, ultimately improving patient outcomes and accessibility to care.