Comparative Evaluation of Large Language Models in Answering Patient Questions Following Periodontal and Peri-Implant...

Large language models (LLMs) are increasingly utilized for medical and dental information retrieval, yet their ability to interpret authentic, patient-style inquiries remains insufficiently investigated. This study compared the performance of ChatGPT, Claude, and Gemini in responding to patient-oriented queries related to periodontal and peri-implant diseases.

Unlike traditional investigations using expert-generated questions, this study employed 40 realistic, patient-oriented queries designed to simulate the post-examination cognitive state, blending colloquial language with partially retained clinical jargon. Each query was submitted to GPT-4o, Claude Sonnet 5 and Gemini 2.5 Pro generating 120 responses. Three blinded periodontists independently evaluated scientific accuracy, completeness, clinical safety, and overall quality using a 5-point Likert scale. Automated text analysis assessed readability metrics (Flesch Reading Ease, Flesch-Kincaid Grade Level, Gunning Fog Index) and linguistic characteristics. Statistical protocols included Friedman, Bonferroni-adjusted Wilcoxon signed-rank, and Model Dominance analyses.

Significant performance differences were observed among the models across all expert-rated domains (all p < 0.001). Gemini achieved the highest expert ratings for scientific accuracy (4.77 ± 0.22), clinical safety (4.94 ± 0.15), and overall quality (4.85 ± 0.20), and was identified as the most frequently top-ranked platform via dominance analysis. Conversely, Claude performed significantly better regarding response completeness (4.77 ± 0.22) and demonstrated the most favorable overall readability profile, yielding the lowest Flesch-Kincaid Grade Level (7.54 ± 1.27). GPT-4o consistently received the lowest expert ratings across all evaluated domains.

While all evaluated LLMs generated high-quality responses to realistic periodontal queries, their functional strengths were highly multidimensional. Gemini demonstrated superior clinical precision and safety, whereas Claude provided more comprehensive and readable explanations. These findings support the integration of LLMs as pragmatic, high-ecological-validity complementary tools for patient education, while emphasizing the persistent necessity for professional clinical oversight.