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

Tirtha Chanda, Sarah Haggenmueller, Tabea-Clara Bucher, Tim Holland-Letz, Harald Kittler, Philipp Tschandl, Markus V. Heppt, Carola Berking, Jochen S. Utikal, Bastian Schilling, Claudia Buerger, Cristian Navarrete-Dechent, Matthias Goebeler, Jakob Nikolas Kather, Carolin V. Schneider, Benjamin Durani, Hendrike Durani, Martin Jansen, Juliane Wacker, Joerg Wacker and Titus J. Brinker ()
Additional contact information
Tirtha Chanda: German Cancer Research Center (DKFZ)
Sarah Haggenmueller: German Cancer Research Center (DKFZ)
Tabea-Clara Bucher: German Cancer Research Center (DKFZ)
Tim Holland-Letz: German Cancer Research Center (DKFZ)
Harald Kittler: Medical University of Vienna
Philipp Tschandl: Medical University of Vienna
Markus V. Heppt: Friedrich-Alexander-Universität Erlangen-Nürnberg
Carola Berking: Friedrich-Alexander-Universität Erlangen-Nürnberg
Jochen S. Utikal: German Cancer Research Center (DKFZ)
Bastian Schilling: Goethe-University Frankfurt
Claudia Buerger: Goethe-University Frankfurt
Cristian Navarrete-Dechent: Pontificia Universidad Católica de Chile
Matthias Goebeler: University Hospital Würzburg
Jakob Nikolas Kather: Faculty of Medicine
Carolin V. Schneider: RWTH University of Aachen
Benjamin Durani: Outpatient Clinic for Dermatology
Hendrike Durani: Outpatient Clinic for Dermatology
Martin Jansen: Outpatient Clinic for Dermatology
Juliane Wacker: Outpatient Clinic for Dermatology
Joerg Wacker: Outpatient Clinic for Dermatology
Titus J. Brinker: German Cancer Research Center (DKFZ)

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-59532-5

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