biostudies-literatureUnknown13Huang M<h4>Background</h4>Large language models (LLMs) are increasingly accessed by lay users for medical advice. This study aims to conduct a comprehensive evaluation of the responses generated by five large language models.<h4>Methods</h4>We identified 31 ophthalmology-related questions most frequently raised by patients during routine consultations and subsequently elicited responses from five large language models: ChatGPT-4o, DeepSeek-V3, Doubao, Wenxin Yiyan 4.0 Turbo, and Qwen. A five-point likert scale was employed to assess each model across five domains: accuracy, logical consistency, coherence, safety, and content accessibility. Additionally, textual characteristics, including character, word, and sentence counts, were quantitatively analyzed.<h4>Results</h4>ChatGPT-4o and DeepSeek-V3 Frontiers in public health1673045https://www.ebi.ac.uk/biostudies/studies/S-EPMC12497754biostudies-literatureComparative performance of large language models for patient-initiated ophthalmology consultations.PMC12497754Yang WZhang ZZhou SHuang MCui XWang XChi WXu YfalseComparative performance of large language models for patient-initiated ophthalmology consultations.<h4>Background</h4>Large language models (LLMs) are increasingly accessed by lay users for medical advice. This study aims to conduct a comprehensive evaluation of the responses generated by five large language models.<h4>Methods</h4>We identified 31 ophthalmology-related questions most frequently raised by patients during routine consultations and subsequently elicited responses from five large language models: ChatGPT-4o, DeepSeek-V3, Doubao, Wenxin Yiyan 4.0 Turbo, and Qwen. A five-point likert scale was employed to assess each model across five domains: accuracy, logical consistency, coherence, safety, and content accessibility. Additionally, textual characteristics, including character, word, and sentence counts, were quantitatively analyzed.<h4>Results</h4>ChatGPT-4o and DeepSeek-V3 2025-01-01T00:00:00Z20252026-05-06T03:19:28.794Z2026-05-06T03:12:35.03ZS-EPMC124977544105918210.3389/fpubh.2025.1673045