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To address this, the paper proposes an AI-enabled prediction framework for skin disease prediction using EN-QNN and Grad-CAM. Initially, the images are collected using IoMT devices of skin diseases and undergo preprocessing, which includes resizing, noise removal and contrast enhancement using AK-CLAHE, followed by color analysis and segmentation using YCbCr and DF-U-Net. Morphological operations are then applied during post-processing. The shape and structure of lesions are analyzed using CMED. Meanwhile, using Grad-CAM, Contextual Information Analysis (CIA) is performed on preprocessed data. Concurrently, disease symptom prediction data (i.e. clinical data) are collected, and features are extracted from this data, including boundary localization and CIA. Finally, skin diseases are classified using EN-QNN. 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