Artificial Intelligence: Practical Primer for Clinical Research in Cardiovascular Disease

Abstract

Artificial intelligence (AI) has begun to permeate and reform the field of medicine and cardiovascular medicine. Impacting about 100 million patients in the United States, the burden of cardiovascular disease is felt in a diverse array of demographics.1, 2 Meanwhile, routine mediums such as multimodality images, electronic health records (EHR), and mobile health devices store troves of underutilized data for each patient. AI has the potential to improve and influence the status quo, with capacity to learn from these massive data and apply knowledge from them to distinct circumstances.3, 4 With considerable information in each heart beat, cardiovascular medicine will definitely be one of the fields that embrace AI to move toward personalized and precise care.5 AI has already been woven into the fabric of everyday life. From an internet search engine, email spam and malware filtering, to uncovering fraudulent credit card purchases, AI addresses an individual's needs in the realms of business, entertainment, and technology. Unfortunately, medicine, including cardiology, has not fully embraced this revolution, with only a limited number of AI‐based clinical applications being available. Nevertheless, there is promise towards routine implementation; machine learning and deep learning have seen an exponential surge of cardiovascular publications in the past decade.6, 7 These methods have proven beneficial in a variety of complex areas including echocardiogram interpretation and diastolic dysfunction grade stratification.8, 9 The US Food and Drug Administration has already approved several devices that utilize AI features.10 Imagine coming to work finding that your system has analyzed all your patients while you were sleeping: their laboratory data, imaging results, symptoms, and mobile device data to calculate their risk of cardiovascular events, death, hospitalization, whether medications should be adjusted/added/removed, or whether they should be referred for an examination. The system presents you with the reasons for its recommendations, and you are confident that they are as good as those given by the most experienced physicians. This may allow you to spend time in shared decision making with your patients, both objectively and compassionately. Although there are currently several barriers/challenges to adoption of AI in clinical practice, undoubtedly, AI will drive current healthcare practice towards a more individualized and precision‐based approach over the next several years. Therefore, general understanding of AI techniques by clinicians and researchers in cardiovascular medicine is paramount. In this review article, we describe the fundamentals of AI that clinicians and researchers should understand, its definition and principles, how to interpret and apply AI in cardiovascular research, limitations, and future perspectives.