Artificial Intelligence, Machine Learning, and Cardiovascular Disease. Issue 14 (September 2020)

• Record Type: Journal Article
• Authors: Mathur, Pankaj; Srivastava, Shweta; Xu, Xiaowei; Mehta, Jawahar L
• Is Part Of: Clinical Medicine Insights. Issue 14(2020)
• Publication Date: 2020-09
• Subjects: AI -- machine learning -- big data -- precision medicine -- cardiovascular disease; Cardiovascular system -- Diseases -- Periodicals; Cardiology -- Periodicals; Cardiovascular Diseases; Cardiology; Cardiovascular system -- Diseases; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals; Periodicals; 616.12
• DOI: 10.1177/1179546820927404
• Languages: English
• ISSNs: 1179-5468

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in the clinical use of AI-based applications and interpretation of the results including data privacy, poorly selected/outdated data, selection bias, and unintentional continuance of historical biases/stereotypes in the data which can lead to erroneous conclusions.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.