ArticleResearch and practice in thrombosis and haemostasis2026
Performance evaluation of 9 large language models in anticoagulation decision-making for nonvalvular atrial fibrillation.
Article in Research and practice in thrombosis and haemostasis, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Background: Large language models (LLMs) are increasingly applied in clinical decision support, but their reliability in guiding anticoagulation management for nonvalvular atrial fibrillation (NVAF) remains unclear. Objectives: To systematically evaluate the performance of 9 widely used LLMs in guiding anticoagulation therapy decisions for NVAF. Methods: A multidisciplinary team constructed 100 virtual NVAF patient cases with comprehensive demographic, clinical, laboratory, and medication information. Nine LLMs (DeepSeek-V3, DeepSeek-R1, Qwen3, Qwen3 Deep Think, ChatGPT-4o, ChatGPT-4o Deep Research, Gemini 2.5, Gemini 2.5 Deep Research, and Claude Sonnet 4) were prompted to provide individualized Congestive heart failure, Hypertension, Age ≥75 years, Diabetes mellitus, Stroke, Vascular disease, Age 65-74 years, Sex category (CHA Results: Across 900 responses, performance varied substantially across 6 clinical tasks. ChatGPT-4o Deep Research achieved the highest accuracy in CHA Conclusion: There was significant variability among LLMs in their decision support for NVAF anticoagulation. Prompt optimization has the potential to markedly enhance accuracy. At present, LLMs may serve as valuable adjuncts to clinical practice but remain unsuitable for use as stand-alone decision-makers.
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