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Active, Not Recruiting

NCT Number: NCT07304908

Effect of Perception-based Interventions on Public Acceptance of Using Large Language Models in Medicine

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.

Active, Not Recruiting

This study is active but is not currently recruiting participants.

Key information

Conditions

Large Language Models Acceptability of Health Care Behavior Health Behavior Patient Acceptance of Health Care Perception, Self Treatment Adherence and Compliance

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Jue Liu

Beijing, Beijing Municipality, 100191, China

About this study

Owing to rapid advances in artificial intelligence, large language models (LLMs) are increasingly being used in a variety of clinical settings such as triage, disease diagnosis, treatment planning, and self-monitoring. Despite their potential, the use of LLMs remains restricted within healthcare settings due to lack of accuracy, coherence, and transparency and ethical concerns. Public perceptions such as perceived usefulness and risks play a crucial role in shaping their attitudes towards artificial intelligence that can either facilitate or hinder its adoption. Yet, to our knowledge, there is lack of awareness about perception-driven interventions in health care and no previous studies have examined whether public perceptions play a role in the acceptability of medical applications of LLMs. Hence, 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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • ≥18 years
  • Capable of completing an online survey
  • Agree to sign an informed consent form

Exclusion criteria

  • Unable to answer questions or communicate
  • Not willing to participate in this study

Treatment and study plan

Perception-based interventions

Other

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.

Primary outcomes

  1. Number of participants who will change their attitudes towards medical applications of large language models

    Time frame: Through study completion, an average of 1 year

    Public acceptance of applying large language models to medicine will be categorized into yes, not sure, and no, which will be collected before perception-based interventions and after interventions.

Sponsors and collaborators

Lead sponsor

Peking University

Other

Collaborators

  • Peking University Third Hospital

Registry information

Official study title

Perception-based Interventions Affect Public Acceptance of Using Large Language Models in Medicine: Randomized Controlled Trial

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Dec 26, 2025
Registry last updated
Dec 26, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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