PURPOSE: The purpose of this study was to evaluate the performance of large language models (LLMs)-GPT-4, GPT-4o, Qwen2, and Qwen2.5-in addressing patient- and clinician-focused questions on ptosis-related inquiries, emphasizing cross-lingual applicability and patient-centric assessment. METHODS: We collected 11 patient-centric and 50 doctor-centric questions covering ptosis symptoms, treatment, and postoperative care. Responses generated by GPT-4, GPT-4o, Qwen2, and Qwen2.5 were evaluated using predefined criteria: accuracy, sufficiency, clarity, and depth (doctor questions); and helpfulness, clarity, and empathy (patient questions). Clinical assessments involved 30 patients with ptosis and 8 oculoplastic surgeons rating responses on a 5-point Likert scale. RESULTS: For doctor questions, GPT-4o outperformed Qwen2.5 in overall performance (53.1% vs. 18.8%, P = 0.035) and completeness (P = 0.049). For patient questions, GPT-4o scored higher in helpfulness (mean rank = 175.28 vs. 155.72, P = 0.035), with no significant differences in clarity or empathy. Qwen2.5 exhibited superior Chinese-language clarity compared to English (P = 0.023). CONCLUSIONS: LLMs, particularly GPT-4o, demonstrate robust performance in ptosis-related inquiries, excelling in English and offering clinically valuable insights. Qwen2.5 showed advantages in Chinese clarity. Although promising for patient education and clinician support, these models require rigorous validation, domain-specific training, and cultural adaptation before clinical deployment. Future efforts should focus on refining multilingual capabilities and integrating real-time expert oversight to ensure safety and relevance in diverse healthcare contexts. TRANSLATIONAL RELEVANCE: This study bridges artificial intelligence (AI) advancements with clinical practice by demonstrating how optimized LLMs can enhance patient education and cross-linguistic clinician support tools in ptosis-related inquiries.