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Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

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Summary

Explainable AI (XAI) boosts dermatologists' melanoma diagnostic accuracy by 2.8% compared to standard AI. Eye-tracking reveals XAI reduces cognitive load, improving trust in AI for medical diagnostics.

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Area of Science:

Dermatology
Artificial Intelligence
Medical Imaging

Background:

Artificial intelligence (AI) enhances diagnostic accuracy in dermatology.
Explainable AI (XAI) aims to improve dermatologist trust and confidence in AI tools.
Objective evaluation of dermatologist interaction with AI and XAI is needed.

Purpose of the Study:

To objectively evaluate dermatologist engagement with AI and XAI systems.
To compare the diagnostic performance of XAI versus standard AI in melanoma detection.
To assess the impact of AI/XAI on dermatologist cognitive load.

Main Methods:

A reader study involving 76 dermatologists diagnosing 16 dermoscopic images.
Utilized an XAI system with domain-specific explanations and eye-tracking technology.
Compared diagnostic accuracy against a standard AI system without explanations.

Main Results:

XAI significantly improved dermatologists' balanced diagnostic accuracy by 2.8% compared to standard AI.
Increased ocular fixations indicated elevated cognitive load during diagnostic disagreements and with complex lesions.
Eye-tracking provided objective measures of dermatologist interaction with AI/XAI.

Conclusions:

XAI enhances diagnostic performance and potentially trust in AI for dermatologists.
Cognitive load is a key factor in AI-assisted medical diagnosis, measurable via eye-tracking.
Findings inform the design of user-centered AI/XAI tools for dermatology and medical diagnostics.