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.

翻译：人工智能系统已显著提升皮肤科医生对黑色素瘤的诊断准确率，而可解释人工智能系统进一步增强了临床医生对AI驱动决策的信心与信任。尽管取得这些进展，如何客观评估皮肤科医生与AI及XAI工具的交互仍存在迫切需求。本研究招募76名皮肤科医生参与阅读实验，使用能提供详细领域特异性解释的XAI系统对16张黑色素瘤与痣的皮肤镜图像进行诊断。研究采用眼动追踪技术评估其交互行为，并将诊断性能与缺乏解释功能的标准AI系统进行对比。研究发现：相较于标准AI系统，XAI系统将平衡诊断准确率提升了2.8个百分点。此外，与AI/XAI系统的诊断分歧及复杂病变均与认知负荷升高相关，具体表现为注视点增加。这些发现对临床实践、视觉任务AI工具设计以及医学诊断中XAI的宏观发展具有重要启示。