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The Diagnostic and Triage Capacity of Laypeople-large Language Model Collaboration in China

NCT07250516 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 6360

Last updated 2025-11-26

No results posted yet for this study

Summary

The goal of this randomized controlled trial is to evaluate the role of large language models in enhancing laypeople's ability to self-diagnose and triage common diseases. The main questions it aims to answer are:

* Does using an LLM help participants make more accurate self-diagnoses and care decisions for common illnesses, compared to their first guess without any help?
* How much better is it when people work together with an LLM, compared to using a regular search engine, using the LLM alone, or how doctors would decide? Researchers will compare participants who were randomly assigned to either the LLM group (using DeepSeek) or the search engine group to see if the LLM-assisted approach leads to better clinical judgments.

Participants will:

* Read one of 48 short, realistic health vignettes;
* Make an initial guess about what might be wrong by listing up to three possible causes, ranked from most to least likely, and choose a care level: seek immediate care, see a doctor within one day, see a doctor within one week, or manage at home without medical care.
* Use their assigned tool (either DeepSeek or a standard search engine) to look up information and update their guess and care decision;
* Submit their final diagnosis and care choice after using the tool. In addition, the study team evaluated the performance of four other AI models (GPT-4o, GPT-o1, DeepSeek-v3, and DeepSeek-r1) and 33 experienced general physicians on the same vignettes.

Conditions

  • Vignette Based Intervention
  • LLM-based AI Dialogue Bot

Interventions

BEHAVIORAL

AI-assisted health information seeking

Participants in this group used a large language model (DeepSeek) to search for medical information related to a clinical vignette after providing initial diagnostic and triage decisions. They were instructed to interact freely with the model to gather insights and then update their diagnoses and triage recommendations. The intervention simulates real-world use of AI tools for personal health decision-making

BEHAVIORAL

Conventional internet search for health information

Participants in this group used mainstream internet search engines (e.g., Baidu, Google, Bing) to look up information about the clinical vignette after making initial diagnostic and triage decisions. They were allowed to search freely but were not permitted to use any named AI chatbot or large language model platform. This group represents typical self-directed online health information seeking behavior.

Sponsors & Collaborators

  • Huazhong University of Science and Technology

    lead OTHER

Principal Investigators

  • Chenxi Liu · Huazhong University of Science and Technology

Study Design

Allocation
RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
SINGLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-04-27
Primary Completion
2025-07-01
Completion
2025-07-01

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07250516 on ClinicalTrials.gov