A Comparative Analysis of Three Large Language Models on Bruxism Knowledge

Artificial Intelligence (AI) has been widely used in health research, but the effectiveness of large language models (LLMs) in providing accurate information on bruxism has not yet been evaluated.

To assess the readability, accuracy and consistency of three LLMs in responding to frequently asked questions about bruxism.

This cross-sectional observational study utilised the Google Trends tool to identify the 10 most frequent topics about bruxism. Thirty frequently asked questions were selected, which were submitted to ChatGPT-3.5, ChatGPT-4 and Gemini at two different times (T1 and T2). The readability was measured using the Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKG) metrics. The responses were evaluated for accuracy using a three-point scale, and consistency was verified by comparing responses between T1 and T2. Statistical analysis included ANOVA, chi-squared tests and Cohen's kappa coefficient considering a p value of 0.5.

In terms of readability, there was no difference in FRE. The Gemini model showed lower FKG scores than the Generative Pretrained Transformer (GPT)-3.5 and GPT-4 models. The average accuracy of the responses was 68.33% for GPT-3.5, 65% for GPT-4 and 55% for Gemini, with no significant differences between the models (p = 0.290). Consistency was substantial for all models, with the highest being in GPT-3.5 (95%). The three LLMs demonstrated substantial agreement between T1 and T2.

Gemini's responses were potentially more accessible to a broader patient population. LLMs demonstrated substantial consistency and moderate accuracy, indicating that these tools should not replace professional dental guidance.