Comparative Evaluation of Five Large Language Models for Orthodontic Patient Communication: A Blinded, Factorial Pilot Study

Background/Objectives: Patients increasingly use large language models (LLMs) to understand orthodontic conditions, yet little is known about how leading models compare on the same patient-communication task or how performance varies with the recipient addressed. Methods: In this blinded pilot, five LLMs—Claude (claude-opus-4-7), GPT (gpt-5.5-2026-04-23), Gemini (gemini-2.5-pro), DeepSeek (deepseek-v4-flash), and Grok (grok-4.3)—each explained three orthodontic malocclusions while adopting three clinician personas and addressing three recipient profiles of differing professional background in a complete balanced 5 × 3 × 3 × 3 design (135 responses in Turkish). Three orthodontists independently scored each response on a seven-axis rubric (1–5). Differences used Kruskal–Wallis, Friedman, and Scheirer–Ray–Hare tests with Holm corrections, epsilon-squared effect sizes, and bootstrap confidence intervals. Results: Inter-rater reliability was good (composite ICC [2, 1] = 0.79). Composite scores differed modestly across models (p = 0.044), but no pairwise model difference survived correction; separation was driven by terminology (p = 0.005). Among factors, the recipient profile showed the largest effect size (epsilon-squared = 0.058 vs. 0.045 for model); the engineer profile scored lower than a healthcare worker or layperson (both corrected p < 0.05), with no statistically significant model × recipient interaction detected. Neither persona nor malocclusion affected quality. Conclusions: Because these were prompted profiles and outcomes were expert-rated, deployment and oversight may benefit from attention to audience fit and terminology rather than model choice alone.