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

Abstract

OBJECTIVE: Artificial intelligence (AI)-driven large language models (LLMs) are increasingly used in patient education; however, their ability to interpret and apply clinical guidelines within real-world physician workflows remains uncertain. Pulmonary embolism (PE), with its well-established diagnostic and management protocols, provides a suitable model for evaluating these systems. This study assessed the performance of four widely used AI-driven LLMs-ChatGPT-4o, DeepSeek-V2, Gemini, and Grok-in applying the 2019 European Society of Cardiology guidelines for PE. The focus was on evaluating clinical accuracy, adherence to guidelines, and response consistency. MATERIAL AND METHODS: Ten open-ended questions based on a simulated PE case were created, covering diagnosis, risk stratification, treatment, and follow-up. Guideline-based reference answers were used for scoring. LLMs were tested under identical conditions, and the responses were anonymized and scored by two emergency physicians using a 10-point scale. Inter-rater reliability was measured using the intraclass correlation coefficient (ICC), and group comparisons were made using Kruskal-Wallis tests. RESULTS: = 0.390). Performance varied by category; ChatGPT-4o excelled in follow-up, while DeepSeek-V2 performed best in diagnostics. Expert reviewers noted ChatGPT-4o's structured responses and Grok's practicality, but highlighted limitations such as insufficient personalization and guideline gaps. Inter-rater agreement was excellent (ICC: 0.986). CONCLUSION: 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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