Summary of Dermatologist-like Explainable Ai Enhances Melanoma Diagnosis Accuracy: Eye-tracking Study, by Tirtha Chanda et al.
Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study
by 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, Reader Study Consortium, Titus J. Brinker
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study investigates how dermatologists engage with explainable AI (XAI) systems compared to standard AI systems when diagnosing melanomas and nevi. A total of 76 dermatologists participated in a reader study using an XAI system that provides detailed explanations for dermoscopic images. The results show that XAI systems improve balanced diagnostic accuracy by 2.8 percentage points, with increased cognitive load associated with disagreements and complex lesions. These findings have significant implications for clinical practice, AI tool design, and the broader development of XAI in medical diagnostics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how doctors use AI tools to diagnose skin cancer. They tested two types of AI systems: one that explains its decisions, called explainable AI (XAI), and one that doesn’t. The doctors looked at pictures of moles and tried to figure out if they were normal or cancerous using both AI systems. The results show that the XAI system is better at making correct diagnoses. It also shows that when the doctor disagrees with the AI, it takes them more time and effort to make a decision. This study helps us understand how doctors use AI tools and how we can make those tools better. |
