Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910614451912704 |
|---|---|
| author | Chanda, Tirtha Haggenmueller, Sarah Bucher, Tabea-Clara Holland-Letz, Tim Kittler, Harald Tschandl, Philipp Heppt, Markus V. Berking, Carola Utikal, Jochen S. Schilling, Bastian Buerger, Claudia Navarrete-Dechent, Cristian Goebeler, Matthias Kather, Jakob Nikolas Schneider, Carolin V. Durani, Benjamin Durani, Hendrike Jansen, Martin Wacker, Juliane Wacker, Joerg Consortium, Reader Study Brinker, Titus J. |
| author_facet | Chanda, Tirtha Haggenmueller, Sarah Bucher, Tabea-Clara Holland-Letz, Tim Kittler, Harald Tschandl, Philipp Heppt, Markus V. Berking, Carola Utikal, Jochen S. Schilling, Bastian Buerger, Claudia Navarrete-Dechent, Cristian Goebeler, Matthias Kather, Jakob Nikolas Schneider, Carolin V. Durani, Benjamin Durani, Hendrike Jansen, Martin Wacker, Juliane Wacker, Joerg Consortium, Reader Study Brinker, Titus J. |
| contents | Artificial intelligence (AI) systems have substantially improved dermatologists' diagnostic accuracy for melanoma, with explainable AI (XAI) systems further enhancing clinicians' 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 participated in a reader study, diagnosing 16 dermoscopic images of melanomas and nevi using an XAI system that provides detailed, domain-specific explanations. Eye-tracking technology was employed to assess their interactions. Diagnostic performance was compared with that of a standard AI system lacking explanatory features. Our findings reveal that XAI systems improved balanced diagnostic accuracy by 2.8 percentage points relative to standard AI. Moreover, diagnostic disagreements with AI/XAI systems and complex lesions were associated with elevated cognitive load, as evidenced by increased ocular fixations. These insights have significant implications for clinical practice, the design of AI tools for visual tasks, and the broader development of XAI in medical diagnostics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13476 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study Chanda, Tirtha Haggenmueller, Sarah Bucher, Tabea-Clara Holland-Letz, Tim Kittler, Harald Tschandl, Philipp Heppt, Markus V. Berking, Carola Utikal, Jochen S. Schilling, Bastian Buerger, Claudia Navarrete-Dechent, Cristian Goebeler, Matthias Kather, Jakob Nikolas Schneider, Carolin V. Durani, Benjamin Durani, Hendrike Jansen, Martin Wacker, Juliane Wacker, Joerg Consortium, Reader Study Brinker, Titus J. Artificial Intelligence Computer Vision and Pattern Recognition Human-Computer Interaction Artificial intelligence (AI) systems have substantially improved dermatologists' diagnostic accuracy for melanoma, with explainable AI (XAI) systems further enhancing clinicians' 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 participated in a reader study, diagnosing 16 dermoscopic images of melanomas and nevi using an XAI system that provides detailed, domain-specific explanations. Eye-tracking technology was employed to assess their interactions. Diagnostic performance was compared with that of a standard AI system lacking explanatory features. Our findings reveal that XAI systems improved balanced diagnostic accuracy by 2.8 percentage points relative to standard AI. Moreover, diagnostic disagreements with AI/XAI systems and complex lesions were associated with elevated cognitive load, as evidenced by increased ocular fixations. These insights have significant implications for clinical practice, the design of AI tools for visual tasks, and the broader development of XAI in medical diagnostics. |
| title | Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2409.13476 |