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
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Locations
-
-
Beijing Municipality
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Beijing, Beijing Municipality, China, 100191
- Jue Liu
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-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
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
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
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No Intervention: Control
No intervention
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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."
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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."
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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.
|
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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."
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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.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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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
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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.
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Through study completion, an average of 1 year
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Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Principal Investigator: Jue Liu, Peking University
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- NNSF72474005
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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