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

XAI systems modestly but meaningfully improve melanoma diagnosis: Explainable AI enhanced balanced diagnostic accuracy by 2.8 percentage points compared to standard AI without explanations, demonstrating that showing doctors why the AI made a decision leads to better patient outcomes.

Explanations build clinician confidence in AI tools: The study found that dermatologists had greater confidence and trust in AI-driven decisions when the system provided detailed, domain-specific explanations—a critical factor for getting doctors to actually use AI in clinical practice.

Eye-tracking reveals cognitive strain when doctors disagree with AI: When dermatologists disagreed with AI/XAI recommendations or encountered complex lesions, their eye-tracking data showed increased cognitive load (more ocular fixations), suggesting these moments require additional mental effort and may be high-risk decision points.

Explainability matters for visual medical AI: For diagnostic tasks involving images, providing explanations alongside AI predictions isn't just nice-to-have—it measurably improves how accurately doctors use the technology, suggesting XAI design should be prioritized in medical imaging applications.

Objective measurement tools can improve AI system design: Using eye-tracking technology to observe how clinicians actually interact with AI systems provides concrete data for designing better tools, moving beyond assumptions about what doctors need to evidence-based development.