“ARTIFICIAL INTELLIGENCE IN THE EARLY DETECTION OF CARDIOVASCULAR DISEASES: CURRENT APPLICATIONS, CHALLENGES, AND FUTURE DIRECTIONS".

Abstract

ABSTRACT: As the primary cause of morbidity and mortality worldwide, cardiovascular diseases (CVDs) highlight the urgent need for better early detection techniques. This narrative review examines how artificial intelligence (AI), particularly deep learning and machine learning, can improve cardiovascular disease risk assessment and early diagnosis. AI-driven methods have shown significant advancements in the analysis of continuous physiological data, cardiac imaging, and ECG signals. Specifically, AI-enhanced ECG makes it possible to identify minute waveform patterns linked to arrhythmias, myocardial infarction, and left ventricular failure, frequently before symptoms appear.Similar to this, AI applications in advanced imaging modalities and echocardiography enable automatic and precise evaluation of heart anatomy and function, lowering inter-observer variability and increasing diagnostic effectiveness. Real-time monitoring and early detection of cardiovascular problems in asymptomatic persons are further supported by wearable device integration. Widespread clinical adoption is nevertheless hampered by issues including data bias, a lack of external validation, algorithm interpretability, and regulatory concerns, despite these advancements. It is anticipated that future advancements in explainable AI models, multimodal data integration, and prospective clinical validation would improve uptake and dependability. Overall, artificial intelligence represents a transformative tool with enormous promise to enhance cardiovascular disease early detection, risk prediction, and individualised treatment.