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.

人工智能（AI）被定义为一组试图模仿人类智能的算法和智能。机器学习就是其中之一，而深度学习就是这些机器学习技术之一。人工智能在包括医院和诊所在内的医疗保健系统中的应用具有许多可能的优势和未来前景。人工智能在心血管医学中的应用是用于诊断程序的机器学习技术，包括成像模式和生物标志物以及用于个性化治疗和改善结果的预测分析。在心血管医学中，基于人工智能的系统在心血管疾病的风险预测、心血管成像、血管重建手术后的结果预测以及新的药物靶点中发现了新的应用。机器学习等人工智能已经部分解决并为介入心脏病学中未满足的需求提供了可能的解决方案。预测经济上重要的终点、具有广泛健康因素的预测模型，包括合并症、社会经济因素和血管造影因素，包括支架尺寸、血管造影期间注入的造影剂体积、支架错位等由于机器学习和人工智能。如今，机器学习技术可能有助于识别高危患者，急性冠脉综合征 (ACS) 后发病率和死亡率更高。通过机器学习的人工智能已经在 ACS 患者中显示出几个潜在的好处。从诊断到治疗效果再到预测 ACS 患者的不良事件和死亡率，机器学习应该在临床医学和介入心脏病学中找到重要的位置，以治疗和管理 ACS 患者。本文是一篇文献综述，重点关注人工智能在 ACS 中的应用。