Performance evaluation of 9 large language models in anticoagulation decision-making for nonvalvular atrial fibrillation

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: -VASc) and Hypertension, Abnormal Renal/Liver Function, Stroke, Bleeding History or Predisposition, Labile INR, Elderly, Drugs/Alcohol Concomitantly (HAS-BLED) scores, anticoagulation contraindications, anticoagulation decisions, regimens, and monitoring recommendations. Outputs were independently evaluated against the multidisciplinary team consensus using accuracy rates and Likert-scale scores, with statistical comparisons performed across models. Results: < .001). 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.