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January 22, 2026Thoracic research and practice1 citationsOpen Access

AI in Patient Care: Evaluating Large Language Model Performance Against Evidence-Based Guidelines for Pulmonary Embolism

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ÖKÖmer Faruk KarakoyunHKH. KoyuncuoǧluÖSÖmer H. Sağnıç

Key Points

  • Evaluate the performance of large language models (LLMs) in managing pulmonary embolism (PE) compared to evidence-based guidelines.
  • Assessment of LLMs in relation to PE management guidelines
  • Comparison of LLM performance across different domains of care
  • Analysis of guideline compliance in AI-generated responses
  • No single LLM consistently excelled across all evaluation domains
  • LLMs demonstrated potential in supporting PE management
  • Need for further development to improve clinical integration and adherence to guidelines

Abstract

AI-driven LLMs show promise in supporting PE management, though none consistently excel in all domains. Further development is needed to enhance clinical integration and guideline compliance.

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Cite This Study

Karakoyun et al. (2026) studied this question.

synapsesocial.com/papers/6971bd26642b1836717e1dbdhttps://doi.org/10.4274/thoracrespract.2025.2025-6-3
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