Big Data and Trustworthy AI for Heart Failure: A Review

Abstract

The rapid evolution of machine learning techniques, combined with the growing availability of large and diverse data sets, is poised to transform heart failure research and clinical care. This review first provides an overview of key machine learning and artificial intelligence concepts used in heart failure research and then examines how diverse data modalities-including electronic health records, patient registries, biobanks, imaging, telemonitoring, and synthetic data-are leveraged to develop machine learning applications for heart failure diagnosis, prognosis, risk stratification, and personalized treatment strategies. While the potential is considerable, we highlight key barriers to clinical translation, such as data heterogeneity, algorithmic bias, lack of interoperability, and privacy concerns. The review also examines the need for explainable and equitable artificial intelligence systems and evaluates emerging solutions, including Federated Learning and synthetic data generation to address fairness and data privacy challenges. Beyond technical innovations, we underscore the importance of human-centered design, stakeholder engagement, and regulatory readiness. We conclude by identifying future priorities and calling for interdisciplinary collaboration to ensure the scalable, ethical, and effective integration of AI in heart failure management.

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