Why the study?
Recent advances in machine learning and the availability of large-scale clinical and multi-omics datasets have driven a surge in artificial intelligence applications in cardiovascular medicine.
Artificial intelligence and machine learning techniques offer promising new perspectives for the diagnosis, prognosis, and personalized treatment of cardiovascular diseases.
May warrant pilot testing of ML tools in select CVD settings; leaves open prospective validation before routine use.
The advent of advances in machine learning (ML)-based techniques has popularized wide applications of artificial intelligence (AI) in various fields ranging from robotics to medicine. In recent years, there has been a surge in the application of AI to research in cardiovascular medicine, which is largely driven by the availability of large-scale clinical and multi-omics datasets. Such applications are providing a new perspective for a better understanding of cardiovascular disease (CVD), which could be used to develop novel diagnostic and therapeutic strategies. For example, studies have shown that ML has a substantial potential for early diagnosis of different types of CVD, prediction of adverse disease outcomes such as heart failure, and development of newer and personalized treatments. In this article, we provide an overview and discuss the current status of a wide range of AI applications, including machine learning, reinforcement learning, and deep learning, in cardiovascular medicine.
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Cheng et al. (2021) studied this question.
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