Interpretable AI for Skin Disease Diagnosis: A Review on Enhancing Trust and Decision-Making in Dermatology
Keywords:
XAI, Interpretable AI, dermatology, LIME, SHAP, Grad-CAMAbstract
AI-driven dermatological diagnosis improves accuracy but faces challenges in interpretability and clinical trust. This review compares deep learning models like CNNs and Transformer-based architectures with techniques such as LIME, SHAP and also Grad-CAM which overcomes black box models by giving explanations for their results. It highlights dataset biases, the need for diverse datasets for better accuracy, and gaps in model generalizability, emphasizing the importance of standardized evaluation and human-AI collaboration for equitable AI adoption in dermatology across India and worldwide.
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