The aggressiveness of prostate cancer, the most common cancer in men worldwide, is primarily assessed based on histopathological data using the Gleason scoring system. While artificial intelligence (AI) has shown promise in accurately predicting Gleason scores, these predictions often lack inherent explainability, potentially leading to distrust in human-machine interactions. To address this issue, we introduce a novel dataset of 1,015 tissue microarray core images, annotated by an international group of 54 pathologists. The annotations provide detailed localized pattern descriptions for Gleason grading in line with international guidelines. Utilizing this dataset, we develop an inherently explainable AI system based on a U-Net architecture that provides predictions leveraging pathologists' terminology. This approach circumvents post-hoc explainability methods while maintaining or exceeding the performance of methods trained directly for Gleason pattern segmentation (Dice score: 0.713 $\pm$ 0.003 trained on explanations vs. 0.691 $\pm$ 0.010 trained on Gleason patterns). By employing soft labels during training, we capture the intrinsic uncertainty in the data, yielding strong results in Gleason pattern segmentation even in the context of high interobserver variability. With the release of this dataset, we aim to encourage further research into segmentation in medical tasks with high levels of subjectivity and to advance the understanding of pathologists' reasoning processes.

Artificial intelligence (AI) systems have substantially improved dermatologists' diagnostic accuracy for melanoma, with explainable AI (XAI) systems further enhancing clinicians' confidence and trust in AI-driven decisions. Despite these advancements, there remains a critical need for objective evaluation of how dermatologists engage with both AI and XAI tools. In this study, 76 dermatologists participated in a reader study, diagnosing 16 dermoscopic images of melanomas and nevi using an XAI system that provides detailed, domain-specific explanations. Eye-tracking technology was employed to assess their interactions. Diagnostic performance was compared with that of a standard AI system lacking explanatory features. Our findings reveal that XAI systems improved balanced diagnostic accuracy by 2.8 percentage points relative to standard AI. Moreover, diagnostic disagreements with AI/XAI systems and complex lesions were associated with elevated cognitive load, as evidenced by increased ocular fixations. These insights have significant implications for clinical practice, the design of AI tools for visual tasks, and the broader development of XAI in medical diagnostics.