Artificial Intelligence and Machine Learning in Cardiovascular Health Care

Abstract

Background: This review article provides an overview of artificial intelligence (AI) and machine learning (ML) as it relates to cardiovascular health care.

Methods: An overview of the terminology and algorithms used in ML as it relates to health care are provided by the author. Articles published up to August 1, 2019, in the field of AI and ML in cardiovascular medicine are also reviewed and placed in the context of the potential role these approaches will have in clinical practice in the future.

Results: AI is a broader term referring to the ability of machines to perform intelligent tasks, and ML is a subset of AI that refers to the ability of machines to learn independently and make accurate predictions. An expanding body of literature has been published using ML in cardiovascular health care. Moreover, ML has been applied in the settings of automated imaging interpretation, natural language processing and data extraction from electronic health records, and predictive analytics. Examples include automated interpretation of chest roentgenograms, electrocardiograms, echocardiograms, and angiography; identification of patients with early heart failure using clinical notes evaluated by ML; and predicting mortality or complications following percutaneous or surgical cardiovascular procedures.

Conclusions: Although there is an expanding body of literature on AI and ML in cardiovascular medicine, the future these fields will have in clinical practice remains to be paved. In particular, there is a promising role in providing automated imaging interpretation, automated data extraction and quality control, and clinical risk prediction, although these techniques require further refinement and evaluation.