Dermatological Disease Diagnosis

Dermatological disease diagnosis is rapidly advancing through the application of artificial intelligence, aiming to improve accuracy, accessibility, and fairness in identifying skin conditions. Current research heavily utilizes deep learning models, including convolutional neural networks (CNNs), vision transformers (ViTs), and large language models (LLMs), often incorporating transfer learning and techniques like contrastive learning and masked autoencoders to address data scarcity and bias. These advancements are focused on improving diagnostic accuracy across diverse skin tones, enhancing explainability for clinicians, and developing robust, efficient, and fair diagnostic tools for broader deployment, particularly in resource-limited settings. The ultimate goal is to improve patient care by enabling earlier and more accurate diagnosis of skin diseases.