Artificial Intelligence, Machine Learning, and Cardiovascular Disease

Abstract

Artificial intelligence (AI)-based applications have found widespread applications in many fields of science, technology, and medicine. The use of enhanced computing power of machines in clinical medicine and diagnostics has been under exploration since the 1960s. More recently, with the advent of advances in computing, algorithms enabling machine learning, especially deep learning networks that mimic the human brain in function, there has been renewed interest to use them in clinical medicine. In cardiovascular medicine, AI-based systems have found new applications in cardiovascular imaging, cardiovascular risk prediction, and newer drug targets. This article aims to describe different AI applications including machine learning and deep learning and their applications in cardiovascular medicine. AI-based applications have enhanced our understanding of different phenotypes of heart failure and congenital heart disease. These applications have led to newer treatment strategies for different types of cardiovascular diseases, newer approach to cardiovascular drug therapy and postmarketing survey of prescription drugs. However, there are several challenges in the clinical use of AI-based applications and interpretation of the results including data privacy, poorly selected/outdated data, selection bias, and unintentional continuance of historical biases/stereotypes in the data which can lead to erroneous conclusions. Still, AI is a transformative technology and has immense potential in health care.

Gene Polymorphisms of the Renin-Angiotensin System and Bleeding Complications of Warfarin: Genetic-Based Machine Learning Models

“…With powerful and fast development, machine learning has been applied to several fields, including medicine [ 16 ]. In terms of cardiology, machine learning can help clinicians by interpreting high-dimensional data (e.g., biomedical and clinical big data, multi-omics data, and images) [ 17 ] and predicting outcomes (e.g., coronary artery disease and heart failure) [ 18 ]. Several studies also have developed machine learning algorithms to predict warfarin dose [ 19 , 20 , 21 ].…”

Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence

“…Artificial intelligence (AI), including deep learning, has gained momentum in recent years and has been successfully applied to a wide range of medical applications [ 119 , 120 , 121 , 122 , 123 , 124 ]; a hot topic of many reviews [ 25 , 26 , 125 ]. In contrast to the traditional survival models, AI models can be trained to recognize complex and nonlinear patterns given a large set of data.…”

Machine learning for predictive data analytics in medicine: A review illustrated by cardiovascular and nuclear medicine examples

“…Machine learning in cardiovascular medicine is widespread used to understand particular phenotypes and to adapt some treatments (Mathur et al., 2020). The main challenge, outlined by Marthur et al ., for machine learning in cardiovascular medicine is the reproducibility of the models obtained, which is strongly influenced by the experimental conditions, the interpretation of the results and the impact of data bias (Mathur et al., 2020). In this part, we detail the thirteen methods found in the cardiovascular field with the bibliographic search.…”