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

Plain-language 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.

Who can participate

What they're measuring

Trial details