
Heart failure (HF) affects over 60 million people worldwide, with challenges persisting despite improved understanding. These include residual risk despite treatment, comprehending HF with preserved ejection fraction (HFpEF), and managing diverse patient data for risk assessment. Artificial intelligence (AI) prediction models might outperform traditional methods in some cases. AI can enhance HF care by aiding decision-making, identifying high-risk patients, and predicting adverse outcomes. This review surveys AI's role in early HF diagnosis, HFpEF characterization, and severity stratification. It examines AI's challenges in clinical application and suggests potential paths for novel algorithms to enhance HF care.
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