Evaluating anti-LGBTQIA+ medical bias in large language models.

ABSTRACT: Large Language Models (LLMs) are increasingly deployed in clinical settings for tasks ranging from patient communication to decision support. While these models demonstrate race-based and binary gender biases, anti-LGBTQIA+ bias remains understudied despite documented healthcare disparities affecting these populations. In this work, we evaluated the potential of LLMs to propagate anti-LGBTQIA+ medical bias and misinformation. We prompted 4 LLMs (Gemini 1.5 Flash, Claude 3 Haiku, GPT-4o, Stanford Medicine Secure GPT [GPT-4.0]) with 38 prompts consisting of explicit questions and synthetic clinical notes created by medically-trained reviewers and LGBTQIA+ health experts. The prompts consisted of pairs of prompts with and without LGBTQIA+ identity terms and explored clinical situations across

SUBMITTER: Chang CT 

PROVIDER: S-EPMC12416741 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature