Application of Artificial Intelligence in Acute Coronary Syndrome: A Brief Literature Review

Artificial intelligence (AI) is defined as a set of algorithms and intelligence to try to imitate human intelligence. Machine learning is one of them, and deep learning is one of those machine learning techniques. The application of AI in healthcare systems including hospitals and clinics has many possible advantages and future prospects. Applications of AI in cardiovascular medicine are machine learning techniques for diagnostic procedures including imaging modalities and biomarkers and predictive analytics for personalized therapies and improved outcomes. In cardiovascular medicine, AI-based systems have found new applications in risk prediction for cardiovascular diseases, in cardiovascular imaging, in predicting outcomes after revascularization procedures, and in newer drug targets. AI such as machine learning has partially resolved and provided possible solutions to unmet requirements in interventional cardiology. Predicting economically vital endpoints, predictive models with a wide range of health factors including comorbidities, socioeconomic factors, and angiographic factors comprising of the size of stents, the volume of contrast agent which was infused during angiography, stent malposition, and so on have been possible owing to machine learning and AI. Nowadays, machine learning techniques might possibly help in the identification of patients at risk, with higher morbidity and mortality following acute coronary syndrome (ACS). AI through machine learning has shown several potential benefits in patients with ACS. From diagnosis to treatment effects to predicting adverse events and mortality in patients with ACS, machine learning should find an essential place in clinical medicine and in interventional cardiology for the treatment and management of patients with ACS. This paper is a review of the literature which will focus on the application of AI in ACS.