Basal cell carcinoma (BCC) accounts for about 75% of skin cancers. The adoption of teledermatology protocols in Spanish public hospitals has increased dermatologists' workload, motivating the development of AI tools for lesion prioritization. However, limited transparency in current systems hinders clinical acceptance. This study proposes an AI system for BCC detection from dermoscopic images that integrates dermatologist diagnostic criteria based on specific dermoscopic patterns. We analyzed 1559 dermoscopic images from 60 primary care centers annotated by four dermatologists for seven BCC patterns. An Expectation-Maximization consensus algorithm was used to build a unified standard reference. A multitask learning model based on MobileNet-V2 was developed to classify lesions and identify clinically relevant patterns, supported by Grad-CAM visual explanations. The system achieved 90% accuracy in BCC classification (precision 0.90, recall 0.89). Clinically relevant BCC patterns were correctly detected in 99% of positive cases, and the pigment network exclusion criterion was satisfied in 95% of non-BCC cases. Grad-CAM maps showed strong spatial agreement with dermatologist-defined regions. The proposed system combines accurate BCC detection with transparent pattern-based explanations, helping bridge the gap between AI performance and clinical trust in teledermatology.

翻译：基底细胞癌（BCC）约占皮肤癌病例的75%。西班牙公立医院远程皮肤病学方案的采用增加了皮肤科医生的工作负荷，这推动了用于病变优先级排序的AI工具的开发。然而，现有系统透明度不足阻碍了临床接受度。本研究提出一种用于皮肤镜图像BCC检测的AI系统，该系统整合了基于特定皮肤镜模式的皮肤科医生诊断标准。我们分析了来自60个初级护理中心的1559张皮肤镜图像，由四位皮肤科医生标注了七种BCC模式。采用期望最大化共识算法构建了统一标准参考。开发了基于MobileNet-V2的多任务学习模型，用于分类病变并识别临床相关模式，并辅以Grad-CAM视觉解释。该系统在BCC分类中达到90%的准确率（精确率0.90，召回率0.89）。临床相关BCC模式在99%的阳性病例中被正确检测，色素网络排除标准在95%的非BCC病例中得到满足。Grad-CAM热图与皮肤科医生标注区域显示出高度空间一致性。所提出的系统将精准的BCC检测与基于模式的透明解释相结合，有助于弥合AI性能与远程皮肤病学临床信任之间的差距。