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

NCT07304908 | Active Not Recruiting

• 1 other identifier: NNSF72474005
• Study Type: interventional
• Target: 3,000
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Large Language Models; Acceptability of Health Care; Perception, Self

Keywords

Large language model; Artificial intelligence; Perception-based interventions; Public acceptance

Outcome Measures

Primary Outcomes (1)

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

  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.

  Through study completion, an average of 1 year

Study Arms (4)

Perceived benefits of large language models in medicine

EXPERIMENTAL

Participants were asked to read "In April 2023, Massachusetts General Hospital launched a pilot program utilizing medical LLMs to assist with emergency department triage and initial diagnosis and observed a reduction in patient wait times and an improvement in clinical efficiency."

Other: Perception-based interventions

Perceived racial bias in large language models in medicine

EXPERIMENTAL

Participants were asked to read "In November 2022, a research team from the University of California, San Francisco found that cutting-edge medical LLMs exhibited racial bias when recommending treatment plans."

Other: Perception-based interventions

Perceived ethical conflicts in large language models in medicine

EXPERIMENTAL

Participants were required to read "In February 2023, a major European hospital network inadvertently leaked partially anonymized but still sensitive patient data during the testing of medical LLMs due to a system configuration error. Although no direct patient harm occurred, this increased public concerns regarding data privacy and security and compelled relevant institutions to conduct urgent reviews of their data protection measures."

Other: Perception-based interventions

Control

NO INTERVENTION

No intervention

Interventions

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.

Perceived benefits of large language models in medicine; Perceived ethical conflicts in large language models in medicine; Perceived racial bias in large language models in medicine

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Peking University: lead
• Peking University Third Hospital: collaborator

Study Sites (1)

Jue Liu

Beijing, Beijing Municipality, 100191, China

Location

MeSH Terms

Conditions

Patient Acceptance of Health Care

Condition Hierarchy (Ancestors)

Treatment Adherence and Compliance; Health Behavior; Behavior

Study Officials

• Jue Liu

  Peking University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Purpose: OTHER
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Prof.

Study Record Dates

• First Submitted: December 1, 2025
• First Posted: December 26, 2025
• Study Start: November 25, 2025
• Primary Completion (Estimated): October 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: December 26, 2025

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)