ArticleAdvances in ophthalmology practice and research
DeepSeek-R1 outperforms Gemini 2.0 Pro, OpenAI o1, and o3-mini in bilingual complex ophthalmology reasoning.
Article in Advances in ophthalmology practice and research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of ethical considerations of large language models in healthcare and medicine.Frontiers in digital health · 2025Pooled it
- Comparison of Two AI Chatbots for Diagnosis and Providing Treatment Suggestions in Retinopathy of Prematurity: Retrospective Study.Journal of medical Internet research · 2026Article
- Leveraging time-series electronic health records with large language models for chronic kidney disease diagnosis in primary care.BMJ health & care informatics · 2026Article
- Artificial intelligence-enabled cytopathological interpretation of fluorescence microscopy images via a multimodal large language model.Quantitative biology (Beijing, China) · 2026Article
- Comparative evaluation of large language models and clinicians in real-world glaucoma clinical reasoning.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Large Language Models for Ophthalmology Training in China: A Prospective Evaluation.Ophthalmology science · 2026Article
- Review
- EyeRAG: graph retrieval-augmented generation for safe and accurate clinical dialogue in ophthalmology.NPJ digital medicine · 2026Article
- Rethinking scale in ophthalmic artificial intelligence: from bigger models to smarter clinical reasoning.NPJ digital medicine · 2026Review
- Comparing the performance of four mainstream large language models on medical literature review generation: a human expert evaluation in SMILE surgery.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Key concept learning for medical vision language model with reasoning capabilities.NPJ digital medicine · 2026Article
- The Performance of DeepSeek R1 and Gemini 3 in Complex Medical Scenarios: Comparative Study.JMIRx med · 2026Article
- Performance of DeepSeek V3, DeepSeek R1, ChatGPT 4o, and ChatGPT o1 on the National Health Professional and Technical Qualification Examination (Intermediate Level) in China: Comparative Analysis.JMIR formative research · 2026Article
- Comparison of the performance of large language models in answering patient questions related to cataract.iScience · 2026Article
- Benchmarking Large Language Models on the Taiwan Neurology Board Examinations (2018-2024): A Comparative Evaluation of GPT-4o, GPT-o1, DeepSeek-V3, and DeepSeek-R1.Bioengineering (Basel, Switzerland) · 2026Article
- Prediction of 12-Week Remission in Patients With Depressive Disorder Using Reasoning-Based Large Language Models: Model Development and Validation Study.JMIR mental health · 2026Article
- Performance comparison of large language models for medication counseling in people living with HIV.Frontiers in public health · 2026Article
- Preliminary evaluation of DeepSeek-R1 and GPT-5.3 in selected PET/CT clinical scenarios: patient preparation, report interpretation, and diagnostic reasoning.Frontiers in medicine · 2026Article
- Can large language models serve as consultants for forensic cause of death analysis? A multidimensional evaluation.Frontiers in artificial intelligence · 2026Article
- From algorithms to operating room: can large language models master China's attending anesthesiology exam? A cross-sectional evaluation.International journal of surgery (London, England) · 2026Article
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6 authors.
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Abstract
Purpose: To evaluate the accuracy and reasoning ability of DeepSeek-R1 and three recently released large language models (LLMs) in bilingual complex ophthalmology cases. Methods: A total of 130 multiple-choice questions (MCQs) related to diagnosis (n = 39) and management (n = 91) were collected from the Chinese ophthalmology senior professional title examination and categorized into six topics. These MCQs were translated into English. Responses from DeepSeek-R1, Gemini 2.0 Pro, OpenAI o1 and o3-mini were generated under default configurations between February 15 and February 20, 2025. Accuracy was calculated as the proportion of correctly answered questions, with omissions and extra answers considered incorrect. Reasoning ability was evaluated through analyzing reasoning logic and the causes of reasoning errors. Results: DeepSeek-R1 demonstrated the highest overall accuracy, achieving 0.862 in Chinese MCQs and 0.808 in English MCQs. Gemini 2.0 Pro, OpenAI o1, and OpenAI o3-mini attained accuracies of 0.715, 0.685, and 0.692 in Chinese MCQs (all Conclusions: DeepSeek-R1 demonstrated superior performance in bilingual complex ophthalmology reasoning tasks than three state-of-the-art LLMs. These findings highlight the potential of advanced LLMs to assist in clinical decision-making and suggest a framework for evaluating reasoning capabilities.
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