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Effect of Perception-based Interventions on Public Acceptance of Using Large Language Models in Medicine
Sponsor: Peking University
Summary
Large language models (LLMs) show promise in medicine, but concerns about their accuracy, coherence, transparency, and ethics remain. To date, public perceptions on using LLMs in medicine and whether they play a role in the acceptability of health care applications of LLMs are not yet fully understood. This study aims to investigate public perceptions on using LLMs in medicine and if interventions for perceptions affect the acceptability of health care applications of LLMs.
Official title: Perception-based Interventions Affect Public Acceptance of Using Large Language Models in Medicine: Randomized Controlled Trial
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
INTERVENTIONAL
Enrollment
3000
Start Date
2025-11-25
Completion Date
2026-12-31
Last Updated
2025-12-26
Healthy Volunteers
Yes
Interventions
Perception-based interventions
Participants allocated to the intervention group received perception-based interventions. Interventions for Groups 1-3 were perceived benefits of LLMs in medicine, perceived racial bias in LLMs in medicine, and perceived ethical conflicts in LLMs in medicine, respectively.
Locations (1)
Jue Liu
Beijing, Beijing Municipality, China