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Accepted for/Published in: JMIR Formative Research

Date Submitted: Oct 31, 2024
Date Accepted: Sep 10, 2025

The final, peer-reviewed published version of this preprint can be found here:

Fact-Checking Large Language Model Responses to a Health Care Prompt: Comparative Study

Ryan P, Davoren O, Elwyn G

Fact-Checking Large Language Model Responses to a Health Care Prompt: Comparative Study

JMIR Form Res 2026;10:e68223

DOI: 10.2196/68223

PMID: 41985066

Fact-checking large language model responses to a healthcare prompt: A comparison study

  • Padhraig Ryan; 
  • Orla Davoren; 
  • Glyn Elwyn

ABSTRACT

Background:

Generative artificial intelligence refers to algorithms that can generate text, images or other data types. Tools such as ChatGPT offer a new opportunity for patients and clinicians to access health information.

Objective:

Evaluate the accuracy and efficiency of automated fact-checking by a large language model.

Methods:

Design: Parallel comparison of a large language model to an expert human using a clinical scenario. Setting: A 23-year-old female questioning the safety of retinoid treatment for acne by sending prompts to a large language model (ChatGPT). Interventions: We asked ChatGPT to suggest improvements to a patient’s initial prompt and compared a clinical expert’s evaluation of the large language model's responses to a fact-check undertaken by the large language model. Outcome measures: Accuracy and consistency of health-related claims and the time to complete fact-checking.

Results:

There was 86% agreement between the clinical expert and the large language model for fact-checking. The expert review took 18 minutes, and the model 42 seconds. The model responses had some inconsistency but had zero fabrication and no obvious omission.

Conclusions:

Large language models can improve prompts and conduct efficient fact-checking. Human experts need to perform additional checks.


 Citation

Please cite as:

Ryan P, Davoren O, Elwyn G

Fact-Checking Large Language Model Responses to a Health Care Prompt: Comparative Study

JMIR Form Res 2026;10:e68223

DOI: 10.2196/68223

PMID: 41985066

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