Large Language Models in Cardiology: A Systematic Review (Preprint)

Abstract

<sec> <title>UNSTRUCTURED</title> Purpose: This review analyzes the application of large language models (LLMs) in the field of cardiology, with a focus on evaluating their performances across various clinical tasks. Methods: We conducted a systematic literature search on PubMed for studies published up to April 14, 2024. Our search strategy involved a combination of multiple keywords pertaining to LLMs and various aspects of cardiology. The risk of bias was evaluated using the QUADAS-2 tool. Results: Fifteen studies met the inclusion criteria, categorized into four domains: chronic and progressive cardiac conditions, acute cardiac events, cardiology education, and cardiac monitoring. Six studies addressing chronic conditions demonstrated variability in the accuracy and depth of LLM-generated responses. In acute cardiac scenarios, three articles showed that LLMs provided medical advice with mixed effectiveness, particularly in delivering CPR instructions. Two studies in educational cardiology revealed high accuracy in answering assessment questions and interpreting clinical cases. Finally, four articles on cardiac diagnostics showed that multimodal LLMs displayed a range of capabilities in ECGs interpretation, with some models performing at or exceeding the level of human specialists. Conclusion: LLMs demonstrate considerable potential in the field of cardiology, particularly in educational applications and routine diagnostics. However, their performance remains inconsistent across various clinical scenarios, particularly in acute care settings where precision is critical. Enhancing LLMs' accuracy in interpreting real-world complex medical data and emergency response guidance is imperative before integration into clinical practice. </sec>

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