Benchmarking publicly accessible large language models for high-myopia multiple-choice question generation in digital ophthalmic education and public health training.

Background: Digital tools are reshaping public health education and training, yet evidence on whether large language models (LLMs) can generate specialist ophthalmic teaching materials remains limited. High myopia (HM), a vision-threatening condition with long-term management needs and public health relevance, provides a suitable setting for evaluating this capability. This study compared five LLMs in generating HM-related multiple-choice questions (MCQs) for ophthalmic education. Methods: Five LLMs (ChatGPT-5.4, Gemini 3, DeepSeek, Kimi K2.5, and Doubao) completed 60 predefined HM MCQ generation tasks each, yielding 300 MCQs. A standardized blueprint covered four domains: basic knowledge, clinical cases, diagnosis and treatment decision-making, and screening/follow-up management. Objective evaluation included structural completeness, format compliance, keyed-answer accuracy, output features, and response time. Two ophthalmology experts rated six domains using 5-point Likert scales, and Spearman analyses examined associations among text features, response time, and expert ratings. Results: All models achieved 100.0% initial structural acceptability, structural completeness, and format compliance. Keyed-answer accuracy was highest for ChatGPT-5.4 and Gemini 3 (both 100.0%), followed by DeepSeek (98.3%) and Kimi K2.5 and Doubao (both 95.0%). Significant between-model differences were observed across all output features and response time (all Conclusion: LLMs can reliably generate structurally valid HM-related MCQs under standardized Chinese prompting conditions. Their value may lie in supporting digital ophthalmic education and public health training, although expert oversight remains necessary because meaningful differences persist in factual accuracy, distractor quality, and direct usability.