Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects

Cardiovascular disease (CVD) represents a significant and increasing burden on healthcare systems. Artificial intelligence (AI) is a rapidly evolving transdisciplinary field employing machine learning (ML) techniques, which aim to simulate human intuition to offer cost-effective and scalable solutio...

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Bibliographic Details
Published in:Vascular health and risk management 2022-01, Vol.18, p.517-528
Main Authors: Haq, Ikram U, Chhatwal, Karanjot, Sanaka, Krishna, Xu, Bo
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Cardiology
Cardiovascular agents
Cardiovascular disease
cardiovascular medicine
Cluster analysis
Coronary heart disease
Datasets
Heart failure
Machine learning
Neural networks
Patient compliance
Review
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Summary:Cardiovascular disease (CVD) represents a significant and increasing burden on healthcare systems. Artificial intelligence (AI) is a rapidly evolving transdisciplinary field employing machine learning (ML) techniques, which aim to simulate human intuition to offer cost-effective and scalable solutions to better manage CVD. ML algorithms are increasingly being developed and applied in various facets of cardiovascular medicine, including and not limited to heart failure, electrophysiology, valvular heart disease and coronary artery disease. Within heart failure, AI algorithms can augment diagnostic capabilities and clinical decision-making through automated cardiac measurements. Occult cardiac disease is increasingly being identified using ML from diagnostic data. Improved diagnostic and prognostic capabilities using ML algorithms are enhancing clinical care of patients with valvular heart disease and coronary artery disease. The growth of AI techniques is not without inherent challenges, most important of which is the need for greater external validation through multicenter, prospective clinical trials. Keywords: artificial intelligence, cardiovascular medicine, machine learning, neural networks
ISSN:1178-2048
1176-6344
1178-2048
DOI:10.2147/VHRM.S279337