  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.
