Large language models for ESC guideline interpretation: a targeted review of accuracy and applicability

Abstract

The European Society of Cardiology (ESC) guidelines provide detailed, evidence-based recommendations for managing cardiovascular diseases. However, their complexity and frequent updates can make them challenging to apply consistently in clinical settings. Artificial intelligence (AI), particularly large language models (LLMs), offers a novel solution by assisting in the interpretation and application of these guidelines more effectively. A narrative review was conducted to assess the role of large language models (LLMs) and related artificial intelligence (AI) systems in supporting the interpretation of ESC guidelines. From 102 records screened, seven studies met the inclusion criteria. Clinical Decision Support Systems (CDSSs) built on ESC guidelines demonstrated improvements in diagnostic accuracy and standardization. Comparative studies revealed that large language models (LLMs), including ChatGPT-4, showed high concordance with expert clinical decisions (up to 86% accuracy for acute coronary syndrome-related questions). Emerging tools, such as MedDoc-Bot, have highlighted the feasibility of direct ESC guideline interpretation by LLMs. LLMs show promise in enhancing clinician understanding and application of ESC guidelines. Although performance is encouraging, further validation and thoughtful integration into clinical practice are necessary to maximize their utility and safety.

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