Maximizing Large Language Model Utility in Cardiovascular Care: A Practical Guide

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 utilized to simplify complex medical information, improve patient-physician communication, and automate tasks such as summarizing medical articles and extracting key information. Additionally, we highlight the role of LLMs in categorizing and analyzing unstructured data, such as medical notes and test results, which could revolutionize data handling and interpretation in cardiovascular research. However, we also emphasize the limitations and challenges associated with LLMs, including potential biases, reasoning opacity, and the need for rigorous validation in medical contexts. This article 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.