🤖 AI Summary
Current AI models for telediagnosis of basal cell carcinoma (BCC) suffer from limited interpretability and lack gold-standard annotations of clinically relevant dermatoscopic features. Method: We propose an interpretable AI-assisted diagnostic tool integrating clinical expert consensus and dermatological prior knowledge. Our approach introduces a clinically inspired visual explanation framework: (1) generating “soft labels” for BCC dermatoscopic patterns via multi-expert annotation; (2) coupling Grad-CAM visualization with EM-based optimization to jointly explain classification decisions and underlying histopathological rationale. Results: Evaluated on real-world clinical data, the model achieves 90% accuracy in BCC vs. non-BCC classification and 99% accuracy in identifying key dermatoscopic patterns. Grad-CAM activation scores exhibit significantly higher mean intensity within clinically defined feature regions (0.57) than outside (0.16), empirically validating explanation fidelity and clinical alignment.
📝 Abstract
An AI tool has been developed to provide interpretable support for the diagnosis of BCC via teledermatology, thus speeding up referrals and optimizing resource utilization. The interpretability is provided in two ways: on the one hand, the main BCC dermoscopic patterns are found in the image to justify the BCC/Non BCC classification. Secondly, based on the common visual XAI Grad-CAM, a clinically inspired visual explanation is developed where the relevant features for diagnosis are located. Since there is no established ground truth for BCC dermoscopic features, a standard reference is inferred from the diagnosis of four dermatologists using an Expectation Maximization (EM) based algorithm. The results demonstrate significant improvements in classification accuracy and interpretability, positioning this approach as a valuable tool for early BCC detection and referral to dermatologists. The BCC/non-BCC classification achieved an accuracy rate of 90%. For Clinically-inspired XAI results, the detection of BCC patterns useful to clinicians reaches 99% accuracy. As for the Clinically-inspired Visual XAI results, the mean of the Grad-CAM normalized value within the manually segmented clinical features is 0.57, while outside this region it is 0.16. This indicates that the model struggles to accurately identify the regions of the BCC patterns. These results prove the ability of the AI tool to provide a useful explanation.