Artificial Intelligence in Precision Cardiovascular Medicine

• Published in: Journal of the American College of Cardiology Vol. 69; no. 21; pp. 2657 - 2664
• Main Authors: Krittanawong, Chayakrit, Zhang, HongJu, Wang, Zhen, Aydar, Mehmet, Kitai, Takeshi
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 30.05.2017; Elsevier Limited
• Subjects: Air pollution; Algorithms; Alzheimer's disease; Arrhythmia; Arteriosclerosis; Artificial Intelligence; Automation; big data; Bleeding; Cameras; Cardiac arrhythmia; Cardiology; Cardiology - methods; Cardiomyopathy; Cardiovascular; Cardiovascular disease; cognitive computing; Computer programs; Deep learning; Gastrointestinal tract; Genotypes; Humans; Learning algorithms; Machine Learning; Media; Neural networks; Neuroimaging; pH effects; Precision medicine; Precision Medicine - methods; Quantitation; Social networks; Statistics; Surgery; Ventricle; Visual perception; United States > US; California; ANN; HTN; AF; STE; AI; ECG; HFpEF; SVM; machine learning; EHR; AUC; CVD; deep learning; ACS; CV; RNN; SCAD; cognitive computing; big data; HF; artificial neural network; artificial intelligence; atrial fibrillation; acute coronary syndrome; heart failure with preserved ejection fraction; cardiovascular disease; heart failure; recurrent neural network; spontaneous coronary artery dissection; area under the curve; cardiovascular; support vector machine; electrocardiographic; speckle-tracking echocardiography; electronic health record; hypertension
• ISSN: 0735-1097; 1558-3597; 1558-3597
• DOI: 10.1016/j.jacc.2017.03.571

Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes, learning capacity, and knowledge storage. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enable cost-effectiveness, and reduce readmission and mortality rates. Over the past decade, several machine-learning techniques have been used for cardiovascular disease diagnosis and prediction. Each problem requires some degree of understanding of the problem, in terms of cardiovascular medicine and statistics, to apply the optimal machine-learning algorithm. In the near future, AI will result in a paradigm shift toward precision cardiovascular medicine. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI’s application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine. [Display omitted]