Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study - Chanda, Tirtha; Haggenmueller, Sarah; Bucher, Tabea-Clara; Holland-Letz, Tim; Kittler, Harald; Tschandl, Philipp; Heppt, Markus V.; Berking, Carola; Utikal, Jochen; Schilling, Bastian; Buerger, Claudia; Navarrete-Dechent, Cristian; et al. (Nature Communications, 2025)
Artificial intelligence (AI) systems substantially improve dermatologists’ diagnostic accuracy for melanoma, with explainable AI (XAI) systems further enhancing their 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 participate in a reader study, diagnosing 16 dermoscopic images of melanomas and nevi using an XAI system that provides detailed, domain-specific explanations, while eye-tracking technology assesses their interactions. Diagnostic performance is compared with that of a standard AI system lacking explanatory features. Here we show that XAI significantly improves dermatologists’ diagnostic balanced accuracy by 2.8 percentage points compared to standard AI. Moreover, diagnostic disagreements with AI/XAI systems and complex lesions are associated with elevated cognitive load, as evidenced by increased ocular fixations. These insights have significant implications for the design of AI/XAI tools for visual tasks in dermatology and the broader development of XAI in medical diagnostics.
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