Building Trust In Ai-Driven Skin Disease Diagnosis Through Explainable Ai

Abstract

This research focuses on developing an AI-based system for skin disease diagnosis that achieves high accuracy while ensuring interpretability and transparency. The proposed system leverages ResNet101 as the backbone model and achieved an impressive 98% accuracy across six skin disease categories: Acne, Carcinoma, Eczema, Keratosis, Milia, and Rosacea. To address the critical challenges of trust and usability in clinical settings, Explainable AI (XAI) techniques, such as LIME, were integrated. These techniques provide detailed visualizations of class-specific probabilities and regional contributions, enabling both patients and dermatologists to better understand and trust the model’s predictions. Extensive experiments were conducted, comparing the performance of ResNet101 against other pre-trained models, including VGG16, ResNet50, and EfficientNetB7. The results highlight the superior feature extraction capabilities and generalization performance of ResNet101, which outperformed other models in accuracy, precision, recall, and F1-score. This research underscores the importance of combining technical accuracy with explainability to enhance trust in AI systems, thereby supporting patient-centered care. By addressing the gap between advanced AI technology and practical healthcare applications, this study contributes to the broad-scale adoption of reliable and transparent AI systems in dermatology and other medical fields.