Abstract
Background: Heart failure (HF) affects over 64 million people globally, imposing substantial morbidity, mortality, and economic burdens. Despite advances in guideline-directed therapies, adherence remains suboptimal due to low health literacy and complex regimens. ChatGPT, an advanced large language model by OpenAI, offers conversational capabilities that could enhance HF education, management, and research. This systematic review synthesizes evidence on ChatGPT's applications in HF, evaluating its accuracy in patient education and question-answering, enhancing readability, and clinical documentation/symptom extraction. Methods: Following PRISMA guidelines, we searched PubMed, Embase, and Cochrane up to July 2025 using the terms "ChatGPT" and "heart failure". Inclusion: Studies on ChatGPT (3.5 or 4) in HF contexts, such as in education, readability and symptom extraction. Exclusion: Non-HF or non-ChatGPT AI. Data extraction covered design, objectives, methods, and outcomes. Thematic synthesis was applied. Results: From 59 records, 7 observational studies were included. Themes included patient education/question-answering (n = 5), readability enhancement (n = 2), and clinical documentation/symptom extraction (n = 1). Accuracy ranged 78-98%, with high reproducibility; readability improved to 6th-7th grade levels; and symptom extraction achieved up to 95% F1 score, outperforming traditional machine learning baselines. Conclusions: ChatGPT shows promise in HF care but requires further randomized validation for outcomes and bias mitigation.