Comparing large language models and search engine responses to common orthodontic questions

Yuanyuan Ren and Jing Sun

PLOS ONE, 2026, vol. 21, issue 1, 1-13

Abstract: Background: Large Language Models (LLMs) highlight their potential in supporting patient education and self-management. Their performance in responses to orthodontic questions has yet to be explored. Objectives: This study aims to compare the quality, empathy, readability, and satisfaction of responses from LLMs and search engines on common orthodontic questions. Methods: Forty-five common orthodontic questions (six categories) and a prompt were developed, and a self-designed multidimensional evaluation questionnaire was constructed. Questions were presented to 5 LLMs and 3 search engines on December,22,2024. The primary outcomes were the median expert-rated scores of LLMs versus search engine responses on quality, empathy, readability, and satisfaction, using 5- or 10-point Likert scales. Results: LLMs scored significantly higher than search engines in quality (4.00 vs. 3.50, p

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0339908

DOI: 10.1371/journal.pone.0339908

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