Cover Image

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

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 ho...

Full description

Saved in:
Bibliographic Details
Main Authors: Chanda, Tirtha (Author) , Haggenmueller, Sarah (Author) , Bucher, Tabea-Clara (Author) , Holland-Letz, Tim (Author) , Kittler, Harald (Author) , Tschandl, Philipp (Author) , Heppt, Markus V. (Author) , Berking, Carola (Author) , Utikal, Jochen (Author) , Schilling, Bastian (Author) , Buerger, Claudia (Author) , Navarrete-Dechent, Cristian (Author) , Goebeler, Matthias (Author) , Kather, Jakob Nikolas (Author) , Schneider, Carolin V. (Author) , Durani, Benjamin (Author) , Durani, Hendrike (Author) , Jansen, Martin (Author) , Wacker, Juliane (Author) , Wacker, Joerg (Author) , Brinker, Titus Josef (Author)
Format: Article (Journal)
Language:English
Published: 21 May 2025
In: Nature Communications
Year: 2025, Volume: 16, Pages: 1-10
ISSN:2041-1723
DOI:10.1038/s41467-025-59532-5
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41467-025-59532-5
Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41467-025-59532-5
Get full text
Author Notes: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 & Titus J. Brinker
Description
Summary: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.
Item Description:Gesehen am 03.09.2025
Physical Description:Online Resource
ISSN:2041-1723
DOI:10.1038/s41467-025-59532-5

Similar Items