Maximising Large Language Model Utility in Cardiovascular Care: A Practical Guide.

Can J Cardiol

Department of Medicine, Montréal Heart Institute, Université de Montréal, Montréal, Canada; Polytechnique Montréal, Montréal, Canada. Electronic address:

Published: October 2024

AI Article Synopsis

  • Large language models (LLMs) have significant potential in improving cardiovascular care and research by simplifying complex medical information and enhancing patient-physician communication.
  • They can automate tasks such as summarizing articles and analyzing unstructured data like medical notes, which could transform how data is interpreted in the field.
  • However, challenges exist, including biases and the need for careful validation, so it's important for cardiovascular professionals to understand both the capabilities and limitations of LLMs.

Article Abstract

Large language models (LLMs) have emerged as powerful tools in artificial intelligence, demonstrating remarkable capabilities in natural language processing and generation. In this article, we explore the potential applications of LLMs in enhancing cardiovascular care and research. We discuss how LLMs can be used to simplify complex medical information, improve patient-physician communication, and automate tasks such as summarising medical articles and extracting key information. In addition, we highlight the role of LLMs in categorising and analysing unstructured data, such as medical notes and test results, which could revolutionise data handling and interpretation in cardiovascular research. However, we also emphasise the limitations and challenges associated with LLMs, including potential biases, reasoning opacity, and the need for rigourous validation in medical contexts. This review provides a practical guide for cardiovascular professionals to understand and harness the power of LLMs while navigating their limitations. We conclude by discussing the future directions and implications of LLMs in transforming cardiovascular care and research.

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Source
http://dx.doi.org/10.1016/j.cjca.2024.05.024DOI Listing

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