Divergent impacts of explainable AI for dermatological diagnosis on clinicians versus lay people.

ABSTRACT: Artificial intelligence (AI) is increasingly permeating healthcare, from serving as a physician assistant to powering consumer applications. The opacity of AI algorithms makes the ability of humans to interact with AI algorithms challenging. To overcome this limitation, explainable AI (XAI) provides insight into AI decision-making, but evidence suggests that XAI can paradoxically induce bias in the human decision-making process. Here we present results from two large-scale experiments, involving 623 lay people and 153 primary care physicians (PCPs), respectively, in which a fairness-based AI model for dermatological diagnoses and different XAI-based explanations were combined to examine how XAI assistance, particularly multimodal large language models (LLMs), influences diagnostic performa

SUBMITTER: Xu X' 

PROVIDER: S-EPMC13472954 | biostudies-literature | 2026 Aug

REPOSITORIES: biostudies-literature