Artificial Intelligence for Cardiovascular Diseases - Lari, Mohd Qasid; Kumar, Deepak; Kumar, Ajay; Murti, Yogesh; Yadav, Prashant Kumar; Kumar, Dileep (Recent Advances in Computer Science and Communications, 2025)
Globally, cardiovascular disease (CVD) continues to be a major cause of death. Advancements in Artificial Intelligence (AI) in recent times present revolutionary opportunities for the diagnosis, treatment, and prevention of this condition. In this paper, we review mainly the applications of AI in CVDs with its limitations and challenges. Artificial intelligence (AI) algorithms can quickly and precisely analyze medical images, such as CT scans, X-rays, and ECGs, helping with early and more accurate identification of a variety of CVD diseases. To identify those who are at a high risk of getting CVD, AI models can also analyze patient data. This allows for early intervention and preventive measures. AI systems are also capable of analyzing complicated medical data to provide individualized therapy recommendations based on the requirements and traits of each patient. During patient meetings, AI-powered solutions can also help healthcare practitioners by offering real-time insights and recommendations, which may improve treatment outcomes. Machine learning (ML), which is a branch of AI and computer sciences, has also been employed to uncover complex interactions among clinical variables, leading to more accurate predictive models for major adverse cardiovascular events (MACE) like combining clinical data with stress test results has improved the detection of myocardial ischemia, enhancing the ability to predict future cardiovascular outcomes. In this paper, we will focus on the current AI applications in different CVDs. Also, precision medicine, and targeted therapy for these cardiovascular problems will be discussed.
Keywords: heart failure