TY - JOUR
T1 - Dermatologist-like explainable AI enhances melanoma diagnosis accuracy
T2 - eye-tracking study
AU - Reader Study Consortium
AU - Chanda, Tirtha
AU - Haggenmueller, Sarah
AU - Bucher, Tabea Clara
AU - Holland-Letz, Tim
AU - Kittler, Harald
AU - Tschandl, Philipp
AU - Heppt, Markus V.
AU - Berking, Carola
AU - Utikal, Jochen S.
AU - Schilling, Bastian
AU - Buerger, Claudia
AU - Navarrete-Dechent, Cristian
AU - Goebeler, Matthias
AU - Kather, Jakob Nikolas
AU - Schneider, Carolin V.
AU - Durani, Benjamin
AU - Durani, Hendrike
AU - Jansen, Martin
AU - Wacker, Juliane
AU - Wacker, Joerg
AU - Kalski, Malin
AU - Klifo, Dietlein
AU - Kiefer, Sebastian
AU - Klifo, Hess
AU - Funk, Timo
AU - Lunderstedt, Jasmin
AU - Buchinger, Amelie
AU - Erdogdu, Ufuk
AU - Weberschock, Tobias
AU - Gosmann, Janika
AU - Sachweizer, Ani
AU - Loos, Sabina
AU - Fahimi, Sarah
AU - Christ, Franca
AU - Dionysia, Danai
AU - Yilmaz, Kaan
AU - Ninosu, Nadia
AU - Schaarschmidt, Marthe Lisa
AU - Baumert, Julia
AU - Sackmann, Tim
AU - Rabe, Leonie
AU - Hoener, Michael
AU - Zieringer, Lisa
AU - Uebel, Christopher
AU - Breakell, Thomas
AU - Sagonas, Ioannis
AU - Bosch-Voskens, Caroline
AU - Sollfrank, Lukas
AU - Rácz, Emõke
AU - Brinker, Titus J.
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2025/12
Y1 - 2025/12
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105005979720
U2 - 10.1038/s41467-025-59532-5
DO - 10.1038/s41467-025-59532-5
M3 - Article
C2 - 40399272
AN - SCOPUS:105005979720
SN - 2041-1723
VL - 16
JO - Nature Communications
JF - Nature Communications
M1 - 4739
ER -