IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-025-59532-5.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)
    University Medical Center Mannheim
    DKFZ Hector Cancer Institute at the University Medical Center Mannheim)

  • 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
    TUD Dresden University of Technology)

  • 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))

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.

Suggested Citation

  • 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 Buer, 2025. "Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59532-5
    DOI: 10.1038/s41467-025-59532-5
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-025-59532-5
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-025-59532-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59532-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.