Vocal Cord Dysfunction: Evaluating the Utility of AI Large Language Models for Patient Education.

Objective: To compare provider preferences for patient education materials generated by OpenEvidence, ChatGPT 5 Extended Thinking, and a laryngologist with fellowship training in response to common patient questions about vocal cord dysfunction (VCD). Study Design: Cross-sectional survey study. Methods: A laryngologist compiled four common patient questions about VCD and authored expert responses. Equivalent responses for patients were generated using OpenEvidence and ChatGPT 5 Extended Thinking with a standardized role prompt, each in a new chat session, and all outputs were limited to four sentences. Responses were de-identified and presented in a REDCap survey. Forty-five healthcare providers ranked responses based on perceived medical accuracy, clarity, and ability to address patient concerns. Preferences were analyzed using chi-square goodness-of-fit and Friedman testing with post hoc pairwise comparisons. Results: Among the 45 respondents, the most represented specialties were otolaryngology ( Conclusion: Responses generated by AI were frequently preferred over a single expert comparator for common VCD questions, with ChatGPT 5 Extended Thinking performing best overall and OpenEvidence showing similar performance on several items. These findings support further evaluation of LLMs as adjunctive tools in laryngology patient education, while highlighting the need for clinician oversight, assessment of individual model performance, and studies of patient outcomes.