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...
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| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
21 May 2025
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| In: |
Nature Communications
Year: 2025, Jahrgang: 16, Pages: 1-10 |
| ISSN: | 2041-1723 |
| DOI: | 10.1038/s41467-025-59532-5 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41467-025-59532-5 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41467-025-59532-5 
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| Verfasserangaben: | 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 |
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| 520 | |a 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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