Artificial Intelligence in the Management of Heart Failure

Abstract

Artificial intelligence (AI) has the potential to revolutionize the management of heart failure. AI-based tools can guide the diagnosis and treatment of known risk factors, identify asymptomatic structural heart disease, improve cardiomyopathy diagnosis and symptomatic heart failure treatment, and uncover patients transitioning to advanced disease. By integrating multimodal data, including omics, imaging, signals, and electronic health records, state-of-the-art algorithms allow for a more tailored approach to patient care, addressing the unique needs of the individual. The past decade has led to the development of numerous AI solutions targeting each aspect of the heart failure syndrome. However, significant barriers to implementation remain and have limited clinical uptake. Data-privacy concerns, real-world model performance, integration challenges, trust in AI, model governance, and concerns about fairness and bias are some of the topics requiring additional research and the development of best practices. This review highlights progress in the use of AI to guide the diagnosis and management of heart failure while underscoring the importance of overcoming key implementation challenges that are currently slowing progress.