Explainable AI as a Double-Edged Sword in Dermatology: The Impact on Clinicians versus The Public

TL;DR

This study investigates how explainable AI influences dermatology diagnostics, revealing that while it can improve accuracy and reduce disparities, it also risks over-reliance and bias, especially among lay users and depending on presentation timing.

Contribution

It provides large-scale experimental evidence on the differential effects of XAI and LLM explanations on clinicians and the public, highlighting potential pitfalls and benefits in medical AI deployment.

Findings

AI assistance improved diagnostic accuracy and fairness across skin tones.

Lay users exhibited increased automation bias with LLM explanations.

Clinicians remained resilient, benefiting regardless of AI correctness.

Abstract

Artificial intelligence (AI) is increasingly permeating healthcare, from physician assistants to consumer applications. Since AI algorithm's opacity challenges human interaction, explainable AI (XAI) addresses this by providing AI decision-making insight, but evidence suggests XAI can paradoxically induce over-reliance or bias. We present results from two large-scale experiments (623 lay people; 153 primary care physicians, PCPs) combining a fairness-based diagnosis AI model and different XAI explanations to examine how XAI assistance, particularly multimodal large language models (LLMs), influences diagnostic performance. AI assistance balanced across skin tones improved accuracy and reduced diagnostic disparities. However, LLM explanations yielded divergent effects: lay users showed higher automation bias - accuracy boosted when AI was correct, reduced when AI erred - while experienced PCPs remained resilient, benefiting irrespective of AI accuracy. Presenting AI suggestions first also led to worse outcomes when the AI was incorrect for both groups. These findings highlight XAI's varying impact based on expertise and timing, underscoring LLMs as a "double-edged sword" in medical AI and informing future human-AI collaborative system design.