Artificial Intelligence in Cardiovascular Diseases: Enhancing Prediction and Treatment - A Concise Review

Abstract

Cardiovascular disease (CVD) continues to be one of most diagnosed and life threatening diseases worldwide, claiming approximately 17.9 million lives yearly. In this landscape it is necessary that we come up with early detection and effective treatment to alleviate patient outcomes. Over the past couple of years, artificial intelligence has considerably enhanced the precision of forecasting Cardiovascular disease risk, diagnosing conditions, and augmenting treatment procedures. This review explores recent developments in the AI-driven approaches, including machine learning, deep learning, and hybrid models, applied to this field of cardiovascular care. This review assesses the efficacy of AI methodologies, underlines challenges in clinical application, and discusses growing technologies such as reinforcement learning, generative models, and digital twins. Issues related to data privacy, model interpretability, and bias are also addressed, with ideas and suggestions for improving scalability and incorporation into the real-world clinical procedures.

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