Artificial Intelligence in Rhinoplasty Recovery: Linguistic Intelligence and Machine Learning-Driven Insights

Objective: This observational, cross-sectional simulation study evaluated ChatGPT-4 as a postoperative information tool for rhinoplasty using standardized questions and blinded ENT specialist ratings. Study Design: This study is an observational, cross-sectional simulation study using blinded expert evaluation. Setting: We used an online Artificial Intelligence (AI) platform accessed under standardized conditions. Methods: Ten typical recovery questions were posed to ChatGPT-4, and the responses were independently rated by ENT specialists for accuracy, clarity, relevance, response time, and patient-centered communication. Responses were also assessed with a structured performance instrument and supported by linguistic and statistical analyses. Results: ChatGPT-4 achieved high scores for accuracy (90%, 95% CI: 84.9–95.1) and clarity (87%, 95% CI: 82.8–91.2), but lower performance in patient-centered communication (77%, 95% CI: 74.0–80.0). Specialist scoring confirmed structured medical reasoning, while machine learning analyses highlighted clarity, diagnostic depth, and empathy as key contributors to higher ratings. Conclusions: ChatGPT-4 demonstrated high clinician-rated accuracy and clarity when answering standardized postoperative rhinoplasty questions, while patient-centered communication remained comparatively lower. These findings suggest that LLM-based tools may complement clinician-delivered postoperative counseling under appropriate oversight, but they are not a substitute for individualized medical advice or surgical follow-up.