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

Chanda T, Haggenmueller S, Bucher TC, Holland-Letz T, Kittler H, Tschandl P, Heppt M, Berking C, Utikal JS, Schilling B, Buerger C, Navarrete-Dechent C, Goebeler M, Kather JN, Schneider CV, Durani B, Durani H, Jansen M, Wacker J, Wacker J, Kalski M, Klifo D, Kiefer S, Klifo H, Funk T, Lunderstedt J, Buchinger A, Erdogdu U, Weberschock T, Gosmann J, Sachweizer A, Loos S, Fahimi S, Christ F, Dionysia D, Yilmaz K, Ninosu N, Schaarschmidt ML, Baumert J, Sackmann T, Rabe L, Höner M, Zieringer L, Uebel C, Breakell T, Sagonas I, Bosch-Voskens C, Sollfrank L, Ronicke M, Kemenes S, Sambale J, Wagner N, Erdmann M, Ammar AM, Manuelyan K, Salerni G, Rácz E, Saa SR, Hoorens I, Salava A, Lengyel Z, Balcere A, Jocic I, Zafirovik Z, Dragolov M, Hudson S, Cenk H, Tsakiri A, Petrovska L, Neto RRO, Ferhatosmanoğlu A, Morales-Sánchez MA, Bondare-Ansberga V, Afiouni R, Erdil DI, Beyens A, Lluch-Galcerá JJ, Vucemilovic AS, Theofilogiannakou P, Sławińska M, Garzona-Navas L, Hartmann D, Ludwig-Peitsch W, Thamm J, Pföhler C, Hoffmann F, Maul JT, Nguyen VA, Braun SA, Gössinger E, Mühleisen B, Feldmeyer L, Bechara FG, Schuh S, Reimer-Taschenbrecker A, Maul LV, Dimitriou F, Persa OD, Welzel J, Ahlgrimm-Siess V, Booken N, Brinker TJ (2025)

Publication Type: Journal article

Publication year: 2025

Journal

Book Volume: 16

Article Number: 4739

DOI: 10.1038/s41467-025-59532-5

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.

Authors with CRIS profile

Involved external institutions

Universitätsklinikum Mannheim / University Medical Centre Mannheim (Universitätsmedizin Mannheim) Germany (DE) Al-Azhar University Egypt (EG) Universitätsspital Basel Switzerland (CH) Luzerner Kantonsspital (LUKS) Switzerland (CH) Ruhr-Universität Bochum (RUB) Germany (DE) Universitätsklinikum Augsburg Germany (DE) Northwestern University United States (USA) (US) Universitätsspital Zürich (USZ) Switzerland (CH) Deutsches Krebsforschungszentrum (DKFZ) Germany (DE) Medizinische Universität Wien Austria (AT) Universitätsklinikum Frankfurt am Main (KGU) Germany (DE) Universitätsklinikum Würzburg Germany (DE) Universitätsklinikum Aachen (UKA) Germany (DE) Universidad Nacional de Rosario (UNR) Argentina (AR) University Medical Center Groningen (UMCG) / Universitair Medisch Centrum Groningen Netherlands (NL) University Hospital Ghent Belgium (BE) Helsinki University Central Hospital (HUCH) / Helsingin seudun yliopistollinen keskussairaala (HYKS) Finland (FI) University of Pécs / Pécsi Tudományegyetem Hungary (HU) Rīga Stradiņš University Latvia (LV) Military Medical Academy (Serbia) / Војномедицинска академија / Vojnomedicinska akademija (VMA) Serbia (RS) Saints Cyril and Methodius University of Skopje / Универзитет „Св. Кирил и Методиј“ во Скопје Republic of North Macedonia (MK) Pamukkale University / Pamukkale Üniversitesi (PAU) Turkey (TR) Public Health Institution Clinical Hospital-Shtip / Јавна Здравствена Установа Клиничка Болница - Штип Republic of North Macedonia (MK) Universidade Federal de Mato Grosso do Sul Brazil (BR) Karadeniz Technical University / Karadeniz Teknik Üniversitesi (KTU) Turkey (TR) Riga 1st hospital / Rīgas 1. slimnīca Latvia (LV) Taksim Eğitim ve Araştırma Hastanesi Turkey (TR) Health Sciences Research Institute of the “Germans Trias i Pujol” Foundation (IGTP) Spain (ES) University Hospital Split Croatia (HR) Universitätskliniken Salzburg Austria (AT) Evangelismos Medical Center Greece (GR) Medical University Gdansk / Gdański Uniwersytet Medyczny Poland (PL) Hospital Clínica Bíblica Costa Rica (CR) München Klinik gGmbH Germany (DE) Vivantes - Netzwerk für Gesundheit GmbH Germany (DE) Universitätsklinikum des Saarlandes (UKS) Germany (DE) Universitätsklinikum Bonn Germany (DE) Universitätsklinikum Münster Germany (DE) Technische Universität München (TUM) Germany (DE) Universitätsklinikum Hamburg-Eppendorf (UKE) Germany (DE)

How to cite

APA:

Chanda, T., Haggenmueller, S., Bucher, T.C., Holland-Letz, T., Kittler, H., Tschandl, P.,... Brinker, T.J. (2025). Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study. Nature Communications, 16. https://doi.org/10.1038/s41467-025-59532-5

MLA:

Chanda, Tirtha, et al. "Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study." Nature Communications 16 (2025).

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