Performance of several large language models when answering common patient questions about type 1 diabetes in children: accuracy, comprehensibility and practicality.

ABSTRACT:

Background

The use of large language models (LLMs) in healthcare has expanded significantly with advances in natural language processing. Models, such as ChatGPT and Google Gemini, are increasingly used to generate human-like responses to questions, including those posed by patients and their families. With the rise in the incidence of type 1 diabetes (T1D) among children, families frequently seek reliable answers regarding the disease. Previous research has focused on type 2 diabetes, but studies on T1D in a pediatric population remain limited. This study aimed to evaluate and compare the performance and effectiveness of different LLMs when answering common questions about T1D.

Methods

This cross-sectional, comparative study used questions frequently asked by children with

SUBMITTER: Ongen YD 

PROVIDER: S-EPMC12512316 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature