Skin diseases constitute a major global healthcare challenge, particularly in regions with limited access to dermatological expertise. Early and accurate diagnosis is essential to reduce disease progression and associated healthcare costs. This research presents SkinSight, an advanced artificial intelligence–based clinical decision support system for automated skin disease detection using digital skin images. The proposed system is designed by aligning a real-world deployable application with state-of-the-art research methodologies reported in recent dermatology-focused deep learning literature. SkinSight integrates an advanced preprocessing pipeline for artifact removal and illumination normalization, a two-stage validation framework to ensure input reliability, and a dual-stream deep learning ensemble combining ResNet50 and InceptionV3 architectures. Additionally, the system incorporates explainable artificial intelligence (XAI) using Grad-CAM to provide visual interpretability of predictions. A comparative analysis between the initial deployment model and the research-grade pipeline is presented, followed by a structured roadmap for bridging implementation gaps. Experimental results demonstrate that aligning deployment architecture with research-level techniques significantly improves robustness, reliability, and clinical trustworthiness. The proposed framework highlights the importance of end- to-end consistency between research and deployment in AI-driven healthcare systems.
Shinde et al. (Thu,) studied this question.