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
References: Add references at CitEc
Citations:
Downloads: (external link)
https://www.nature.com/articles/s41467-025-59532-5 Abstract (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59532-5
Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/
DOI: 10.1038/s41467-025-59532-5
Access Statistics for this article
Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie
More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().