Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study

Chanda T., Haggenmueller S., Bucher T., Holland-Letz T., Kittler H., Tschandl P., ...More

Nature Communications, vol.16, no.1, 2025 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 16 Issue: 1
  • Publication Date: 2025
  • Doi Number: 10.1038/s41467-025-59532-5
  • Journal Name: Nature Communications
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, CAB Abstracts, Chemical Abstracts Core, Geobase, INSPEC, MEDLINE, Veterinary Science Database, Directory of Open Access Journals, Nature Index
  • Karadeniz Technical University Affiliated: Yes

Abstract

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.