Chronic heart failure (CHF) is a prevalent and multifactorial condition associated with a significant burden of morbidity and mortality. Despite progress in its clinical management, the projected increase in CHF prevalence due to population ageing, increased cardiovascular risk burdens, and advancing diagnostic and therapeutic options have led to a growing burden on healthcare systems and public budgets worldwide. In this context, artificial intelligence (AI) holds promise in assisting clinical decision-making, especially in analysing raw image data and electrocardiogram recordings. This article provides an overview of the current gaps and needs in CHF research and clinical management and the current and under-development AI-powered tools that may address these gaps and needs.
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