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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月21日

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  • 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,
  • Nina Booken,
  • Verena Ahlgrimm-Siess,
  • Julia Welzel,
  • Oana-Diana Persa,
  • Florentia Dimitriou,
  • Stephan Alexander Braun,
  • Lara Valeska Maul,
  • Antonia Reimer-Taschenbrecker,
  • Sandra Schuh,
  • Falk G. Bechara,
  • Laurence Feldmeyer,
  • Beda Mühleisen,
  • Elisabeth Gössinger,
  • Stephan Alexander Braun,
  • Van Anh Nguyen,
  • Julia-Tatjana Maul,
  • Friederike Hoffmann,
  • Claudia Pföhler,
  • Janis Thamm,
  • Wiebke Ludwig-Peitsch,
  • Daniela Hartmann,
  • Laura Garzona-Navas,
  • Martyna Sławińska,
  • Panagiota Theofilogiannakou,
  • Ana Sanader Vucemilovic,
  • Juan José Lluch-Galcerá,
  • Aude Beyens,
  • Dilara Ilhan Erdil,
  • Rym Afiouni,
  • Vanda Bondare-Ansberga,
  • Martha Alejandra Morales-Sánchez,
  • Arzu Ferhatosmanoğlu,
  • Roque Rafael Oliveira Neto,
  • Lidija Petrovska,
  • Amalia Tsakiri,
  • Hülya Cenk,
  • Sharon Hudson,
  • Miroslav Dragolov,
  • Zorica Zafirovik,
  • Ivana Jocic,
  • Alise Balcere,
  • Zsuzsanna Lengyel,
  • Alexander Salava,
  • Isabelle Hoorens,
  • Sonia Rodriguez Saa,
  • Emõke Rácz,
  • Gabriel Salerni,
  • Karen Manuelyan,
  • Amr Mohammad Ammar,
  • Michael Erdmann,
  • Nicola Wagner,
  • Jannik Sambale,
  • Stephan Kemenes,
  • Moritz Ronicke,
  • Lukas Sollfrank,
  • Caroline Bosch-Voskens,
  • Ioannis Sagonas,
  • Thomas Breakell,
  • Christopher Uebel,
  • Lisa Zieringer,
  • Michael Hoener,
  • Leonie Rabe,
  • Tim Sackmann,
  • Julia Baumert,
  • Marthe Lisa Schaarschmidt,
  • Nadia Ninosu,
  • Kaan Yilmaz,
  • Danai Dionysia,
  • Franca Christ,
  • Sarah Fahimi,
  • Sabina Loos,
  • Ani Sachweizer,
  • Janika Gosmann,
  • Tobias Weberschock,
  • Ufuk Erdogdu,
  • Amelie Buchinger,
  • Jasmin Lunderstedt,
  • Timo Funk,
  • Hess Klifo,
  • Sebastian Kiefer,
  • Dietlein Klifo,
  • Malin Kalski,
  • Titus J. Brinker

摘要

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.