ArticleTaiwan journal of ophthalmology
Evaluating large language models for glaucoma counseling: A pilot study of ChatGPT and Google Gemini responses in Traditional Chinese.
Article in Taiwan journal of ophthalmology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Accuracy and Effectiveness of AI-Powered Systems in Patient Counseling, Education, and Management in Optometry and Related Eye-Care Settings: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
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6 authors.
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Abstract
purposeLarge language models (LLMs) such as ChatGPT and Google Gemini are increasingly used for patient education in various medical fields. However, their effectiveness in providing ophthalmology-related information in non-English languages remains underexplored. This study aimed to evaluate and compare the appropriateness of ChatGPT and Google Gemini responses to glaucoma-related questions presented in Traditional Chinese. MATERIALS AND
methodsTwenty frequently asked glaucoma-related questions were translated into Traditional Chinese and categorized into four domains: disease understanding, diagnosis, treatment, and lifestyle. Responses were generated using ChatGPT (GPT-4) and Google Gemini. Three qualified ophthalmologists independently rated the appropriateness of each response using a 5-point Likert scale. Mean scores were calculated, and statistical comparisons were performed for overall and category-specific performance.
resultsChatGPT achieved a higher overall appropriateness score (4.32 ± 0.65) than Google Gemini (4.03 ± 0.66), with a statistically significant difference (
conclusionBoth ChatGPT and Google Gemini are capable of providing reasonably appropriate glaucoma-related information in Traditional Chinese. ChatGPT demonstrated superior overall performance. These findings support the potential role of LLMs in enhancing patient education in non-English settings, although further improvements in language localization and domain-specific accuracy are warranted.
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