A Clinically Grounded and Uncertainty-Aware Explainable Artificial Intelligence Detailed Analysis for Skin Lesion Diagnosis
TL;DR
A conceptual framework is proposed that integrates dermoscopic image analysis, lesion segmentation, deep learning-based prediction, clinical concept recognition, uncertainty estimation, clinical reasoning, and structured explanation and distinguishes the reliability of the prediction from the reliability of the explanation.
Abstract
Artificial intelligence (AI), specifically machine learning and deep learning, has demonstrated remarkable potential for the automated diagnosis of skin lesions using dermoscopic images. While deep learning models can achieve high classification performance, significant barriers to clinical adoption remain, including limited interpretability, uncertainty in model predictions, dataset bias, and limited clinical validation. This review examines the development of AI-based skin lesion diagnosis placing special emphasis on explainable AI (XAI), clinically meaningful concept representation, uncertainty estimation, and dermatologist-centred evaluation. The reviewed literature suggests that traditional pixel-level interpretation methods can identify image regions associated with model predictions but fail to effectively communicate clinically meaningful evidence. In contrast, clinically grounded approaches can link visual information to recognizable dermatological concepts such as asymmetry, border characteristics colour variation, diameter-related features, and relevant dermoscopic structures. This paper synthesizes these developments and highlights critical research gaps regarding concept-level interpretation, uncertainty-aware interpretation, longitudinal assessment, dataset generalization, and clinical validation. Based on this synthesis, a conceptual framework is proposed that integrates dermoscopic image analysis, lesion segmentation, deep learning-based prediction, clinical concept recognition, uncertainty estimation, clinical reasoning, and structured explanation. This framework distinguishes the reliability of the prediction from the reliability of the explanation and characterizes concepts as clearly detected undetected uncertain or non assessable particular attention is paid to progression, which cannot be reliably determined from a single dermoscopic image without longitudinal evidence. The proposed framework aims to support transparent and clinically meaningful AI-assisted skin lesion assessment while maintaining the dermatologist as the ultimate clinical decision-maker. Future research should involve quantitative validation of the framework through dermatologist-centred studies and evaluate the quality of explanations, consistency, uncertainty calibration, and clinical utility.
