Artificial intelligence in heart failure

Abstract

Heart failure (HF) is a growing and challenging syndrome that has become a global pandemic, given its increasing burden on communities and healthcare systems. Standard classical methods have been unsuccessful in screening for and identifying HF at an early stage where pharmacologic and nonpharmacologic interventions could be more effective and improve survival. In past decades, artificial intelligence (AI) has become involved in almost all aspects of life, including health care. AI has been applied in various ways to overcome the known and unknown challenges of HF. The current model of categorizing HF patients into two groups based on their ejection fraction has many limitations, making it necessary to use AI methods to recategorize HF patients into new groups. With a better understanding of different HF phenotypes, we can discover novel therapies to improve long-term survival. When AI-enhanced models were developed and applied to electrocardiograms, echocardiographic images, and electronic health records, new, inexpensive, and noninvasive methods successfully screened for HF on a population level. The application of AI can be expanded to predict HF exacerbations and prevent hospitalization. With the promising roles of AI in HF, we should be better able to understand, treat, and improve survival from this still poorly understood and broad syndrome.

Publication Title

Intelligence Based Cardiology and Cardiac Surgery Artificial Intelligence and Human Cognition in Cardiovascular Medicine

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