Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects.
Ikram U HaqKaranjot ChhatwalKrishna SanakaBo XuPublished in: Vascular health and risk management (2022)
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.
Keyphrases
- artificial intelligence
- machine learning
- big data
- coronary artery disease
- heart failure
- deep learning
- healthcare
- atrial fibrillation
- left ventricular
- cardiovascular disease
- clinical trial
- decision making
- endothelial cells
- percutaneous coronary intervention
- pulmonary hypertension
- cardiovascular events
- current status
- coronary artery bypass grafting
- palliative care
- cardiac resynchronization therapy
- aortic valve
- acute heart failure
- electronic health record
- randomized controlled trial
- quality improvement
- cross sectional
- oral anticoagulants
- risk factors
- health information