Skin Disease

Skin disease research heavily focuses on developing accurate and equitable AI-based diagnostic tools, addressing significant disparities in model performance across different skin tones and disease prevalence. Current efforts utilize various deep learning architectures, including Vision Transformers, convolutional neural networks (CNNs), and hybrid models, often incorporating transfer learning and domain adaptation techniques to improve robustness and fairness. These advancements aim to improve early detection and diagnosis of skin diseases, ultimately enhancing healthcare access and outcomes, particularly for underrepresented populations. The development of diverse and high-quality datasets, coupled with explainable AI methods, is crucial for building reliable and trustworthy diagnostic systems.