Artificial Intelligence in Cardiovascular Disease Detection: A Superficial Overview

Abstract

In this comprehensive review, we delve into the rapidly evolving domain of Artificial Intelligence (AI) methodologies employed in the detection and diagnosis of cardiovascular diseases - the globe's leading cause of mortality. These methods, which harbor immense potential to revolutionize healthcare, span from complex Machine Learning algorithms, Deep Learning models, and Neural Networks, to cutting-edge advancements in Natural Language Processing. Diverse in their mechanics, these AI-driven techniques exhibit varying levels of predictive precision and are often commingled to enhance diagnostic accuracy. Notably, we shine light on convolutional neural networks (CNNs), which have shown unprecedented promise in analyzing medical imaging for heart disease detection. Furthermore, we scrutinize the intriguing paradox of AI systems; while their sophistication permits extraordinary diagnostic precision, their perplexity and opacity often create barriers to widespread acceptance. Our review also explores real-world applications, effectiveness, and the ethical dimensions surrounding AI in cardiovascular disease detection. Ultimately, the article seeks to convey the transformative potential of AI methodologies in cardiovascular medicine and address the vital need for a holistic understanding of their mechanisms, benefits, and limitations.