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Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Author

Listed:
  • Tirtha Chanda

    (German Cancer Research Center (DKFZ))

  • Katja Hauser

    (German Cancer Research Center (DKFZ))

  • Sarah Hobelsberger

    (University Hospital, Technical University Dresden)

  • Tabea-Clara Bucher

    (German Cancer Research Center (DKFZ))

  • Carina Nogueira Garcia

    (German Cancer Research Center (DKFZ))

  • Christoph Wies

    (German Cancer Research Center (DKFZ)
    Medical Faculty of University Heidelberg)

  • Harald Kittler

    (Medical University of Vienna)

  • Philipp Tschandl

    (Medical University of Vienna)

  • Cristian Navarrete-Dechent

    (Pontificia Universidad Católica de Chile)

  • Sebastian Podlipnik

    (University of Barcelona, IDIBAPS)

  • Emmanouil Chousakos

    (National & Kapodistrian University of Athens)

  • Iva Crnaric

    (Sestre milosrdnice University Hospital Center)

  • Jovana Majstorovic

    (Derma Style, Dermatovenerology clinic)

  • Linda Alhajwan

    (Dubai London Clinic)

  • Tanya Foreman

    (West Dermatology, Newport Beach)

  • Sandra Peternel

    (Clinical Hospital Center Rijeka, Faculty of Medicine, University of Rijeka)

  • Sergei Sarap

    (LaserMed)

  • İrem Özdemir

    (Faculty of Medicine, Gazi University)

  • Raymond L. Barnhill

    (Unit of Formation and Research of Medicine University of Paris)

  • Mar Llamas-Velasco

    (Universidad Autónoma de Madrid)

  • Gabriela Poch

    (Venereology and Allergology)

  • Sören Korsing

    (University Hospital Essen, University Duisburg-Essen)

  • Wiebke Sondermann

    (Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg)

  • Frank Friedrich Gellrich

    (University Hospital, Technical University Dresden)

  • Markus V. Heppt

    (Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg)

  • Michael Erdmann

    (Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg)

  • Sebastian Haferkamp

    (University Hospital Regensburg)

  • Konstantin Drexler

    (University Hospital Regensburg)

  • Matthias Goebeler

    (Venereology and Allergology, University Hospital Würzburg)

  • Bastian Schilling

    (Venereology and Allergology, University Hospital Würzburg)

  • Jochen S. Utikal

    (Venereology and Allergology, University Medical Center Mannheim, Ruprecht-Karl University of Heidelberg)

  • Kamran Ghoreschi

    (Venereology and Allergology)

  • Stefan Fröhling

    (National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ))

  • Eva Krieghoff-Henning

    (German Cancer Research Center (DKFZ))

  • Titus J. Brinker

    (German Cancer Research Center (DKFZ))

Abstract

Artificial intelligence (AI) systems have been shown to help dermatologists diagnose melanoma more accurately, however they lack transparency, hindering user acceptance. Explainable AI (XAI) methods can help to increase transparency, yet often lack precise, domain-specific explanations. Moreover, the impact of XAI methods on dermatologists’ decisions has not yet been evaluated. Building upon previous research, we introduce an XAI system that provides precise and domain-specific explanations alongside its differential diagnoses of melanomas and nevi. Through a three-phase study, we assess its impact on dermatologists’ diagnostic accuracy, diagnostic confidence, and trust in the XAI-support. Our results show strong alignment between XAI and dermatologist explanations. We also show that dermatologists’ confidence in their diagnoses, and their trust in the support system significantly increase with XAI compared to conventional AI. This study highlights dermatologists’ willingness to adopt such XAI systems, promoting future use in the clinic.

Suggested Citation

  • Tirtha Chanda & Katja Hauser & Sarah Hobelsberger & Tabea-Clara Bucher & Carina Nogueira Garcia & Christoph Wies & Harald Kittler & Philipp Tschandl & Cristian Navarrete-Dechent & Sebastian Podlipnik , 2024. "Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-43095-4
    DOI: 10.1038/s41467-023-43095-4
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    References listed on IDEAS

    as
    1. Leone, Daniele & Schiavone, Francesco & Appio, Francesco Paolo & Chiao, Benjamin, 2021. "How does artificial intelligence enable and enhance value co-creation in industrial markets? An exploratory case study in the healthcare ecosystem," Journal of Business Research, Elsevier, vol. 129(C), pages 849-859.
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    1. Shadi, Mohammad Reza & Mirshekali, Hamid & Shaker, Hamid Reza, 2025. "Explainable artificial intelligence for energy systems maintenance: A review on concepts, current techniques, challenges, and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 216(C).

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