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

NCT ID: NCT07304908

Last Updated: 2025-12-26

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 3000 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-11-25
• Study Completion Date: 2026-12-31

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.

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.

Conditions

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

Keywords

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

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: OTHER
• Blinding Strategy: SINGLE

Study Groups

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."

Group Type: EXPERIMENTAL

Perception-based interventions

Intervention Type: 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 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."

Group Type: EXPERIMENTAL

Perception-based interventions

Intervention Type: 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 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."

Group Type: EXPERIMENTAL

Perception-based interventions

Intervention Type: 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.

Control

No intervention

Group Type: NO_INTERVENTION

No interventions assigned to this group

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.

Intervention Type: OTHER

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Peking University Third Hospital

OTHER

Sponsor Role: collaborator

Peking University

OTHER

Sponsor Role: lead

Responsible Party

Liu Jue

Prof.

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Jue Liu

Role: PRINCIPAL_INVESTIGATOR

Peking University

Locations

Jue Liu

Beijing, Beijing Municipality, China

Countries

China

Other Identifiers

NNSF72474005

Identifier Type: -

Identifier Source: org_study_id