The Impact of Artificial Intelligence on Cardiovascular Disease Diagnosis: A Review

Abstract

Background: Cardiovascular diseases present a significant global health challenge and remain the leading cause of death worldwide. However, traditional approaches to prevention, diagnosis, and treatment struggle to keep up with the increasing prevalence of these diseases.

Aim: To enhance patient outcomes and optimize healthcare resource utilization. Artificial intelligence (AI), specifically machine learning and deep learning, has rapidly emerged as a promising tool with the potential to revolutionize various aspects of cardiovascular disease management, including detection, diagnosis, and treatment.

Method: Reviewed the current literature surrounding AI techniques using PubMed, Science Direct, NCBI and Google Scholar, specifically exploring machine learning and deep learning, and their application in diagnosing heart disease. The focus was on AI's role in improving diagnostic techniques such as echocardiography, cardiac magnetic resonance imaging, computed tomography angiography, and electrocardiogram analysis.

Results: AI has promising applications in various aspects of cardiovascular disease management. Its application in diagnostic techniques can help detect, diagnose, and treat heart disease, ultimately leading to more accurate and personalized treatments.

Practical Implication: By integrating these advanced technologies into clinical practice, we can transform the diagnosis and management of heart diseases, leading to more accurate and personalized diagnostics and treatments.

Conclusion: AI presents a significant potential in transforming the global health landscape by enhancing cardiovascular disease management. By leveraging these advanced technologies, clinicians can improve patient care and overall outcomes while addressing the increasing prevalence of these diseases.

Keywords: Heart Diseases, Diagnosis, Deep Learning, Machine Learning, Public Health.