A Primer on Large Language Models (LLMs) and ChatGPT for Cardiovascular Healthcare Professionals.

Generative artificial intelligence (AI), particularly large language models (LLMs), such as ChatGPT, is transforming healthcare by offering novel ways to synthesize and communicate medical knowledge. This development is especially relevant in cardiology, as patient education, clinical decision-making, and administrative workflows play pivotal roles in this area. ChatGPT, originally built on GPT-3 and refined into GPT-4, can simplify complex cardiology literature, translate technical explanations into plain language, and address questions across different linguistic backgrounds. Studies show that although ChatGPT demonstrates considerable promise in performing text-based tasks-ranging from passing portions of the European Exam in Core Cardiology to creating patient-friendly educational materials-its inability to interpret images remains a major limitation. Meanwhile, concerns around false information, data bias, and ethical issues highlight the need for careful oversight. Future directions include integrating LLMs with computer-vision modules for image-based diagnostics and combining unstructured patient data to improve risk prediction and phenotyping. Social-media research suggests that chatbots sometimes provide more-empathetic responses than do physicians, underscoring both their potential advantages and complexities. LLM-based tools can also generate letters for insurance prior authorizations or appeals, helping reduce administrative burden. New multimodal approaches, such as ChatGPT Vision, have the potential to enable direct image processing, although clinical validation of this function is yet to be established. The judicious integration of ChatGPT and other LLMs into cardiology requires ongoing validation, robust regulatory frameworks, and strong ethical guidelines to ensure patient privacy, avoid misinformation, and promote equitable healthcare delivery. This review aims to provide a primer on LLMs for cardiovascular professionals, summarizing key applications, current limitations, and prospects in this rapidly evolving field of digital health.