ArticleEye (London, England)2025
Comparative performance analysis of global and chinese-domain large language models for myopia.
Article in Eye (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Large Language Models for Ophthalmology Training in China: A Prospective Evaluation.Ophthalmology science · 2026Article
- Benchmarking publicly accessible large language models for high-myopia multiple-choice question generation in digital ophthalmic education and public health training.Frontiers in public health · 2026Article
- Evaluating Large Language Models in Ptosis-Related inquiries: A Cross-Lingual Study.Translational vision science & technology · 2025Article
- To take a different approach: Can large language models provide knowledge related to respiratory aspiration?Digital healthArticle
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13 authors.
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Abstract
backgroundThe performance of global large language models (LLMs), trained largely on Western data, for disease in other settings and languages is unknown. Taking myopia as an illustration, we evaluated the global versus Chinese-domain LLMs in addressing Chinese-specific myopia-related questions.
methodsGlobal LLMs (ChatGPT-3.5, ChatGPT-4.0, Google Bard, Llama-2 7B Chat) and Chinese-domain LLMs (Huatuo-GPT, MedGPT, Ali Tongyi Qianwen, and Baidu ERNIE Bot, Baidu ERNIE 4.0) were included. All LLMs were prompted to address 39 Chinese-specific myopia queries across 10 domains. 3 myopia experts evaluated the accuracy of responses with a 3-point scale. "Good"-rating responses were further evaluated for comprehensiveness and empathy using a five-point scale. "Poor"-rating responses were further prompted for self-correction and re-analysis.
resultsThe top 3 LLMs in accuracy were ChatGPT-3.5 (8.72 ± 0.75), Baidu ERNIE 4.0 (8.62 ± 0.62), and ChatGPT-4.0 (8.59 ± 0.93), with highest proportions of 94.8% "Good" responses. Top five LLMs with comprehensiveness were ChatGPT-3.5 (4.58 ± 0.42), ChatGPT-4.0 (4.56 ± 0.50), Baidu ERNIE 4.0 (4.44 ± 0.49), MedGPT (4.34 ± 0.59), and Baidu ERNIE Bot (4.22 ± 0.74) (all p ≥ 0.059, versus ChatGPT-3.5). While for empathy were ChatGPT-3.5 (4.75 ± 0.25), ChatGPT-4.0 (4.68 ± 0.32), MedGPT (4.50 ± 0.47), Baidu ERNIE Bot (4.42 ± 0.46), and Baidu ERNIE 4.0 (4.34 ± 0.64) (all p ≥ 0.052, versus ChatGPT-3.5). Baidu ERNIE 4.0 did not receive a "Poor" rating, while others demonstrated self-correction capabilities, showing enhancements ranging from 50% to 100%.
conclusionsGlobal and Chinese-domain LLMs demonstrate effective performance in addressing Chinese-specific myopia-related queries. Global LLMs revealed optimal performance in Chinese-language settings despite primarily training with non-Chinese data and in English.
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