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

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

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Perception, Self; Acceptability of Health Care; Large Language Models
• Intervention / Treatment: Other: Perception-based interventions

Detailed Description

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.

• Study Type: Interventional
• Enrollment (Estimated): 3000
• Phase: Not Applicable

Contacts and Locations

Study Locations

• China
  • Beijing Municipality
    • Beijing, Beijing Municipality, China, 100191
      • Jue Liu

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Other
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

4

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
No Intervention: Control; No intervention
Experimental: Perceived benefits of large language models in medicine; 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; 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.
Experimental: Perceived racial bias in large language models in medicine; 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; 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.
Experimental: Perceived ethical conflicts in large language models in medicine; 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; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Peking University
• Collaborators: Peking University Third Hospital
• Investigators: Principal Investigator: Jue Liu, Peking University

Study record dates

Study Major Dates

• Study Start (Actual): November 25, 2025
• Primary Completion (Estimated): October 31, 2026
• Study Completion (Estimated): December 31, 2026

Study Registration Dates

• First Submitted: December 1, 2025
• First Submitted That Met QC Criteria: December 11, 2025
• First Posted (Actual): December 26, 2025

Study Record Updates

• Last Update Posted (Actual): December 26, 2025
• Last Update Submitted That Met QC Criteria: December 11, 2025
• Last Verified: December 1, 2025

More Information

Terms related to this study

• Keywords: Artificial intelligence; Large language model; Perception-based interventions; Public acceptance
• Additional Relevant MeSH Terms: Behavior; Treatment Adherence and Compliance; Health Behavior; Patient Acceptance of Health Care
• Other Study ID Numbers: NNSF72474005

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No