Exposure to content written by large language models can reduce stigma around opioid use disorder.

ABSTRACT: Widespread stigma, both offline and online, hinders harm reduction efforts in the context of opioid use disorder (OUD). This stigma targets clinically approved medications for OUD (MOUD), people with the condition, and the condition itself, among several others. Given the potential of artificial intelligence in promoting health equity, this work examines whether large language models (LLMs) can abate stigmatizing attitudes in virtual healthcare communities. To answer this, we conducted a series of randomized controlled experiments, where participants read LLM-generated, human-written, or no responses to help-seeking OUD-related content. The experiment was conducted under two setups: participants read the responses either once (N = 2, 141) or repeatedly for 14 days (N = 107). Participants r

SUBMITTER: Mittal S 

PROVIDER: S-EPMC12669018 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature