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Comparing large language models and search engine responses to common orthodontic questions
1School of Nursing, Peking University, Beijing, China.
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 < 0.001), empathy (3.75 vs. 3.50, p < 0.001), readability (4.00 vs. 3.75, p < 0.001), and satisfaction (8.00 vs. 7.25, p < 0.001). LLM-generated responses were rated significantly higher than those from search engines in therapeutic outcomes category, appliance selection category and cost category.
Conclusions:
In this cross-sectional study, the LLMs, particularly GPT-4o, outperformed search engines. These results indicate the potential of LLMs as supplementary tools for orthodontic patient education and self-management.
