ArticleJournal of medical systems2025
Evaluation of DeepSeek-R1 for Ophthalmic Diagnosis and Reasoning: A Comparison with OpenAI o1 and o3.
Article in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Adaptive Fast-Slow Large Language Model Framework for Multidimensional Classification of Prenatal Ultrasound Reports: Comparative Study.Journal of medical Internet research · 2026Article
- Evaluating large language model clinical reasoning in glaucoma using retrieval-augmented generation.Advances in ophthalmology practice and researchArticle
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7 authors.
Funding
Abstract
backgroundDeepSeek-R1, an open-source reasoning large language model (LLM) clinically deployed in Chinese hospitals, still lacks validation in ophthalmology.
aimsTo compare DeepSeek-R1 against OpenAI's o1 and upgraded o3 models in diagnostic accuracy and reasoning capability across diverse ophthalmic conditions.
methodsWe evaluated 98 standardized case vignettes covering13 ophthalmic sub-specialties, each supplied with an expert-validated diagnostic hierarchy, differential list, and reasoning chain. Model performance was assessed with a diagnosis matrix focused on final-diagnosis (FDx) accuracy; incorrect outputs were resubmitted with key diagnostic clues (reasoning-augmented, RA prompt) to test self-correction. Reasoning capacity was quantified by the number/score of diagnostic clues retrieved per case across 13 predefined domains.
resultsDeepSeek-R1 achieved an 87.8% FDx accuracy, comparable to o3 (91.8%, P = .34) and higher than o1 (58.2%, P < .001). Similar trends were observed for others accuracy (global P < .001). Agreement was moderate-high between R1 and o3 (κ = 0.42-1.00), but slight with o1 (κ = 0.12-0.32). R1 and o3 identified more diagnostic clues than o1 (median count = 4 vs. 3, median score = 100 vs. 80; P < .001). RA prompts corrected 50.0%, 62.5% and 41.5% of FDx errors for R1, o3, and o1, raising FDx accuracy to 93.9%, 96.9%, and 80.6% respectively.
conclusionsDeepSeek-R1 matched o3 and outperformed o1 in diagnostic accuracy and reasoning, retrieving nearly all expert-defined clues. Its open-source nature, low cost and strong performance support its use as a practical aid for ophthalmic decision-making.
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