DeepSeek-R1 Outperforms Gemini 2.0 Pro, OpenAI o1, and o3-mini in Bilingual Complex Ophthalmology Reasoning

• URL: http://arxiv.org/abs/2502.17947v1
• Date: Tue, 25 Feb 2025 08:08:53 GMT
• Title: DeepSeek-R1 Outperforms Gemini 2.0 Pro, OpenAI o1, and o3-mini in Bilingual Complex Ophthalmology Reasoning
• Abstract summary: DeepSeek-R1 and three other recently released large ophthalmology reasoning models (LLMs) were evaluated.<n>DeepSeek-R1 achieved the highest accuracy across five topics in both Chinese English and Chinese MCQs.<n> Reasoning ability was evaluated through analyzing reasoning logic and the causes of reasoning error.
• Score: 4.050281368901217
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Purpose: To evaluate the accuracy and reasoning ability of DeepSeek-R1 and three other 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 using DeepSeek-R1. The responses of 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 error. 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 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 medical data, and too aggressive were the most common causes of reasoning errors. Conclusion: DeepSeek-R1 demonstrated superior performance in bilingual complex ophthalmology reasoning tasks than three other state-of-the-art LLMs. While its clinical applicability remains challenging, it shows promise for supporting diagnosis and clinical decision-making.

Related papers

• ThReadMed-QA: A Multi-Turn Medical Dialogue Benchmark from Real Patient Questions [5.63130104359934]
  ThReadMed-QA is a benchmark of 2,437 fully-answered patient-seeking conversation threads extracted from r/AskDocs.<n>We evaluate five state-of-the-art LLMs on a stratified test split of 238 conversations.<n>Even the strongest model, GPT-5, achieves only 41.2% fully-correct responses.
  arXiv  Detail & Related papers  (2026-03-11T20:17:57Z)
• PanCanBench: A Comprehensive Benchmark for Evaluating Large Language Models in Pancreatic Oncology [48.732366302949515]
  Large language models (LLMs) have achieved expert-level performance on standardized examinations, yet multiple-choice accuracy poorly reflects real-world clinical utility and safety.<n>We developed a human-in-the-loop pipeline to create expert rubrics for de-identified patient questions.<n>We evaluated 22 proprietary and open-source LLMs using an LLM-as-a-judge framework, measuring clinical completeness, factual accuracy, and web-search integration.
  arXiv  Detail & Related papers  (2026-03-02T00:50:39Z)
• Assessing LLMs' Performance: Insights from the Chinese Pharmacist Exam [9.07457306513003]
  In China, the national pharmacist exam serves as a standardized benchmark for evaluating pharmacists' clinical and theoretical competencies.<n>This study aimed to compare the performance of two large language models: ChatGPT-4o and DeepSeek-R1.
  arXiv  Detail & Related papers  (2025-11-25T17:31:25Z)
• Evaluation of Causal Reasoning for Large Language Models in Contextualized Clinical Scenarios of Laboratory Test Interpretation [1.3321309556812102]
  This study evaluates causal reasoning in large language models (LLMs) using 99 clinically grounded laboratory test scenarios.<n>We examined common laboratory tests such as hemoglobin A1c, creatinine, and vitamin D, and paired them with relevant causal factors including age, gender, obesity, and smoking.<n>Two models - GPT-o1 and Llama-3.2-8b-instruct - were tested, with responses evaluated by four medically trained human experts.
  arXiv  Detail & Related papers  (2025-09-19T19:28:12Z)
• MedQARo: A Large-Scale Benchmark for Medical Question Answering in Romanian [50.767415194856135]
  We introduce MedQARo, the first large-scale medical QA benchmark in Romanian.<n>We construct a high-quality and large-scale dataset comprising 102,646 QA pairs related to cancer patients.
  arXiv  Detail & Related papers  (2025-08-22T13:48:37Z)
• MedOmni-45°: A Safety-Performance Benchmark for Reasoning-Oriented LLMs in Medicine [69.08855631283829]
  We introduce Med Omni-45 Degrees, a benchmark designed to quantify safety-performance trade-offs under manipulative hint conditions.<n>It contains 1,804 reasoning-focused medical questions across six specialties and three task types, including 500 from MedMCQA.<n>Results show a consistent safety-performance trade-off, with no model surpassing the diagonal.
  arXiv  Detail & Related papers  (2025-08-22T08:38:16Z)
• ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning [54.30630356786752]
  ReasonMed is the largest medical reasoning dataset to date, with 370k high-quality examples.<n>It is built through a multi-agent generation, verification, and refinement process.<n>Using ReasonMed, we find that integrating detailed CoT reasoning with concise answer summaries yields the most robust fine-tuning results.
  arXiv  Detail & Related papers  (2025-06-11T08:36:55Z)
• Knowledge or Reasoning? A Close Look at How LLMs Think Across Domains [52.86636270242863]
  This work moves beyond the final-answer accuracy and investigates step-by-step reasoning in the medical and mathematical domains.<n>We introduce a fine-grained evaluation framework that judges the correctness of knowledge used and the quality of reasoning.<n>Using this framework, we study R1-distilled and base Qwen models trained with supervised fine-tuning (SFT) and/or reinforcement learning (RL) in the medical and math domains.
  arXiv  Detail & Related papers  (2025-06-02T18:01:00Z)
• Disentangling Reasoning and Knowledge in Medical Large Language Models [23.401484250342158]
  Medical reasoning in large language models aims to emulate clinicians' diagnostic thinking.<n>Current benchmarks such as MedQA-USMLE, MedMCQA, and PubMedQA often mix reasoning with factual recall.<n>We evaluate biomedical models (HuatuoGPT-o1, MedReason, m1) and general-domain models (DeepSeek-R1, o4-mini, Qwen3)<n>We train BioMed-R1 using fine-tuning and reinforcement learning on reasoning-heavy examples.
  arXiv  Detail & Related papers  (2025-05-16T17:16:27Z)
• R-Bench: Graduate-level Multi-disciplinary Benchmarks for LLM & MLLM Complex Reasoning Evaluation [75.33671166231096]
  We introduce a graduate-level, multi-disciplinary, EnglishChinese benchmark, dubbed as Reasoning Bench (R-Bench)<n>RBench spans 1,094 questions across 108 subjects for language model evaluation and 665 questions across 83 subjects for multimodal model testing.<n>We evaluate widely used models, including OpenAI o1, GPT-4o, DeepSeek-R1, etc.
  arXiv  Detail & Related papers  (2025-05-04T07:48:36Z)
• ChestX-Reasoner: Advancing Radiology Foundation Models with Reasoning through Step-by-Step Verification [57.22053411719822]
  ChestX-Reasoner is a radiology diagnosis MLLM designed to leverage process supervision mined directly from clinical reports. Our two-stage training framework combines supervised fine-tuning and reinforcement learning guided by process rewards to better align model reasoning with clinical standards.
  arXiv  Detail & Related papers  (2025-04-29T16:48:23Z)
• Benchmarking Next-Generation Reasoning-Focused Large Language Models in Ophthalmology: A Head-to-Head Evaluation on 5,888 Items [5.087573829521397]
  This study comprehensively evaluated and compared the accuracy and reasoning capabilities of four newly developed reasoning-focused LLMs. O1 and DeepSeek-R1 achieved the highest accuracy, with o1 also leading in Macro-F1 (0.900). The performance of models across the text-generation metrics varied: o3-mini excelled in ROUGE-L (0.151), o1 in METEOR (0.232), DeepSeek-R1 and o3-mini tied for BERTScore (0.673), DeepSeek-R1 (-4.105) and Gemini 2.0 Flash-Thinking (-4.127) performed best in BART
  arXiv  Detail & Related papers  (2025-04-15T13:42:34Z)
• LLMs in Disease Diagnosis: A Comparative Study of DeepSeek-R1 and O3 Mini Across Chronic Health Conditions [0.0]
  DeepSeek R1 achieved a disease-level accuracy of 76% and an overall accuracy of 82%, outperforming O3 Mini. Both models, however, struggled with Respiratory Disease classification, recording accuracies of only 40% for DeepSeek R1 and 20% for O3 Mini.
  arXiv  Detail & Related papers  (2025-03-13T15:54:26Z)
• Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases [48.87360916431396]
  We introduce MedR-Bench, a benchmarking dataset of 1,453 structured patient cases, annotated with reasoning references. We propose a framework encompassing three critical examination recommendation, diagnostic decision-making, and treatment planning, simulating the entire patient care journey. Using this benchmark, we evaluate five state-of-the-art reasoning LLMs, including DeepSeek-R1, OpenAI-o3-mini, and Gemini-2.0-Flash Thinking, etc.
  arXiv  Detail & Related papers  (2025-03-06T18:35:39Z)
• RuozhiBench: Evaluating LLMs with Logical Fallacies and Misleading Premises [41.39610589639382]
  We present RuozhiBench, a dataset containing 677 carefully curated questions that contain various forms of deceptive reasoning.<n>We evaluate 17 large language models (LLMs) from 5 Series over RuozhiBench using both open-ended and two-choice formats.<n>LLMs showed limited ability to detect and reason correctly about logical fallacies, with even the best-performing model, Claude-3-haiku, achieving only 62% accuracy compared to the human of more than 90%.
  arXiv  Detail & Related papers  (2025-02-18T18:47:11Z)
• LLM Assistance for Pediatric Depression [2.1398676192061683]
  This work investigates the feasibility of state-of-the-art LLMs for depressive symptom extraction in pediatric settings (ages 6-24).<n>Our findings show that all LLMs are 60% more efficient than word match, with Flan leading in precision (average F1: 0.65, precision: 0.78), excelling in the extraction of more rare symptoms like "sleep problems" (F1: 0.92) and "self-loathing" (F1: 0.8)<n>Llama 3, with the highest recall (0.90), overgeneralizes symptoms, making it less suitable for this type of analysis.
  arXiv  Detail & Related papers  (2025-01-29T09:27:27Z)
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning [147.16121855209246]
  We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1.<n>DeepSeek-R1-Zero is trained via large-scale reinforcement learning.<n>DeepSeek-R1 incorporates multi-stage training and cold-start data before RL.
  arXiv  Detail & Related papers  (2025-01-22T15:19:35Z)
• Can OpenAI o1 Reason Well in Ophthalmology? A 6,990-Question Head-to-Head Evaluation Study [6.883286555555795]
  This study evaluated OpenAI o1 and five LLMs using 6,990 ophthalmological questions from MedMCQA.<n>O1 achieved the highest accuracy (0.88) and macro-F1 score but ranked third in reasoning capabilities based on text-generation metrics.
  arXiv  Detail & Related papers  (2025-01-20T02:40:01Z)
• LLM Robustness Against Misinformation in Biomedical Question Answering [50.98256373698759]
  The retrieval-augmented generation (RAG) approach is used to reduce the confabulation of large language models (LLMs) for question answering. We evaluate the effectiveness and robustness of four LLMs against misinformation in answering biomedical questions.
  arXiv  Detail & Related papers  (2024-10-27T16:23:26Z)
• Language Enhanced Model for Eye (LEME): An Open-Source Ophthalmology-Specific Large Language Model [25.384237687766024]
  We introduce an open-source, specialized LLM for ophthalmology, termed Language Enhanced Model for Eye (LEME) LEME was initially pre-trained on the Llama2 70B framework and further fine-tuned with a corpus of 127,000 non-copyrighted training instances. We benchmarked LEME against eight other LLMs, namely, GPT-3.5, GPT-4, three Llama2 models (7B, 13B, 70B), PMC-LLAMA 13B, Meditron 70B, and EYE-Llama.
  arXiv  Detail & Related papers  (2024-10-01T02:43:54Z)
• WellDunn: On the Robustness and Explainability of Language Models and Large Language Models in Identifying Wellness Dimensions [46.60244609728416]
  Language Models (LMs) are being proposed for mental health applications where the heightened risk of adverse outcomes means predictive performance may not be a litmus test of a model's utility in clinical practice. We introduce an evaluation design that focuses on the robustness and explainability of LMs in identifying Wellness Dimensions (WDs) We reveal four surprising results about LMs/LLMs.
  arXiv  Detail & Related papers  (2024-06-17T19:50:40Z)
• Automatically measuring speech fluency in people with aphasia: first achievements using read-speech data [55.84746218227712]
  This study aims at assessing the relevance of a signalprocessingalgorithm, initially developed in the field of language acquisition, for the automatic measurement of speech fluency.
  arXiv  Detail & Related papers  (2023-08-09T07:51:40Z)
• Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Dataset [31.047827145874844]
  We introduce CMExam, sourced from the Chinese National Medical Licensing Examination. CMExam consists of 60K+ multiple-choice questions for standardized and objective evaluations, as well as solution explanations for model reasoning evaluation in an open-ended manner. For in-depth analyses of LLMs, we invited medical professionals to label five additional question-wise annotations, including disease groups, clinical departments, medical disciplines, areas of competency, and question difficulty levels.
  arXiv  Detail & Related papers  (2023-06-05T16:48:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.