Novel Artificial Intelligence Applications in Cardiology: Current Landscape, Limitations, and the Road to Real-World Applications.
Élodie Labrecque LanglaisPascal Thériault-LauzierGuillaume Marquis-GravelMerve KulbayDerek Y SoJean-François TanguayHung Q LyRichard GalloFrédéric LesageRobert AvramPublished in: Journal of cardiovascular translational research (2022)
Cardiovascular diseases are the leading cause of death globally and contribute significantly to the cost of healthcare. Artificial intelligence (AI) is poised to reshape cardiology. Using supervised and unsupervised learning, the two main branches of AI, several applications have been developed in recent years to improve risk prediction, allow large-scale analysis of medical data, and phenotype patients for personalized medicine. In this review, we examine the key advances in AI in cardiology and its limitations regarding bias in the data, standardization in reporting, data access, and model trust and accountability in cases of error. Finally, we discuss implementation methods to unleash AI's potential in making healthcare more accurate and efficient. Several steps need to be followed and challenges overcome in order to successfully integrate AI in clinical practice and ensure its longevity.
Keyphrases
- artificial intelligence
- big data
- machine learning
- healthcare
- deep learning
- electronic health record
- end stage renal disease
- clinical practice
- cardiovascular disease
- cardiac surgery
- chronic kidney disease
- newly diagnosed
- thoracic surgery
- peritoneal dialysis
- primary care
- type diabetes
- acute kidney injury
- health information
- adverse drug
- prognostic factors
- data analysis
- mass spectrometry
- metabolic syndrome
- single cell
- social media
- drosophila melanogaster
- human health