DeepSeek-R1 outperforms Gemini 2.0 Pro, OpenAI o1, and o3-mini in bilingual complex ophthalmology reasoning

To evaluate the accuracy and reasoning ability of DeepSeek-R1 and three recently released large language models (LLMs) in bilingual complex ophthalmology cases.

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.

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 P ​<0.001 compared with DeepSeek-R1), and 0.746 (P ​= ​0.115), 0.723 (P ​= ​0.027), and 0.577 (P ​<0.001) in English MCQs, respectively. DeepSeek-R1 achieved the highest accuracy across five topics in both Chinese and English MCQs. It also excelled in management questions conducted in Chinese (all P ​<0.05). Reasoning ability analysis showed that the four LLMs shared similar reasoning logic. Ignoring key positive history, ignoring key positive signs, misinterpretation of medical data, and overuse of non-first-line interventions were the most common causes of reasoning errors.

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.