Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: eye-tracking study
This research addresses the need for better AI tools in medical diagnostics, specifically for dermatologists diagnosing melanoma, but it is incremental as it builds on existing XAI advancements with a focus on evaluation.
The study tackled the problem of objectively evaluating how dermatologists interact with explainable AI (XAI) tools for melanoma diagnosis, finding that XAI improved balanced diagnostic accuracy by 2.8 percentage points compared to standard AI and that diagnostic disagreements and complex lesions increased cognitive load as measured by eye-tracking.
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.