What type of study is this?

This is a Cross-Sectional Study study.

October 11, 2025BMC PediatricsOpen Access

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

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Authors

YÖYasemin Denkboy Öngen AAAyla İrem Aydın MAMeryem Atak

Discussion

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Overview

Cross-sectional comparative analysis reveals large language models can provide reliable answers in pediatric diabetes, suggesting their potential role in healthcare.

Key Points

Large language models showed similar performance when answering common questions about type 1 diabetes in children.
ChatGPT-4o achieved the highest mean score of 3.78 ± 1.09, while Gemini scored the lowest at 3.40 ± 1.24.
The evaluation used a standard prompt and was assessed by pediatric endocrinologists using the General Quality Scale.
Despite no significant differences, the study highlights the promise of advanced models in providing patient-friendly answers.

Cite This Study

Öngen et al. (2025) studied this question.

synapsesocial.com/papers/68ea72339f1bd4df558cede9 https://doi.org/10.1186/s12887-025-05945-6

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October 11, 2025BMC PediatricsOpen Access

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

View Full Paper

Ask AI

Bookmark

Authors

YÖYasemin Denkboy Öngen AAAyla İrem Aydın MAMeryem Atak

Discussion

Member takes

Overview

Cross-sectional comparative analysis reveals large language models can provide reliable answers in pediatric diabetes, suggesting their potential role in healthcare.

Key Points

Large language models showed similar performance when answering common questions about type 1 diabetes in children.
ChatGPT-4o achieved the highest mean score of 3.78 ± 1.09, while Gemini scored the lowest at 3.40 ± 1.24.
The evaluation used a standard prompt and was assessed by pediatric endocrinologists using the General Quality Scale.
Despite no significant differences, the study highlights the promise of advanced models in providing patient-friendly answers.

Cite This Study

Öngen et al. (2025) studied this question.

synapsesocial.com/papers/68ea72339f1bd4df558cede9 https://doi.org/10.1186/s12887-025-05945-6

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