Large language models versus expert clinicians in optimizing Chinese patient education materials for temporomandibular disorders: a mixed-methods study integrating readability, accuracy, actionability, and cultural adaptability.

Objective: To evaluate the performance of large language models (LLMs) and expert clinicians in optimizing Chinese patient education materials (PEMs) for temporomandibular disorders (TMD) across readability, accuracy, actionability, and cultural adaptability, and to determine whether a human-AI collaboration model can achieve an optimal balance among these dimensions. Methods: Fifteen TMD education topics were selected through a Delphi consensus process. Four groups of PEMs were generated: original texts (Group A), LLM-rewritten texts (Group B, Claude Opus 4.6), clinician-rewritten texts (Group C), and human-AI collaboration texts (Group D). Blinded assessments were conducted by a professional panel ( Results: LLM-rewritten texts demonstrated significantly shorter sentences (21.1 vs. 33.9 characters, Conclusion: The human-AI collaboration model represents a promising and balanced approach for producing Chinese TMD patient education materials, combining LLM strengths in structural optimization with expert clinician oversight for accuracy and cultural appropriateness.