Harnessing Artificial Intelligence & Machine Learning in Treatment of Cardiovascular Disorders: A Review

Abstract

The advent of AI and ML in healthcare has led to a complete metamorphosis in how we diagnose, treat, and manage CVDs. To illustrate how AI/ML might influence patient benefit and clinical workflow optimization, this review explores the most recent developments and the recent clinical implementations on treating CVD. AI and ML technology assist in risk evaluation, early diagnosis, and personalized cure protocols by employing adept algorithms and data analytics. These include predictive models that can analyze vast amounts of clinical data for patterns and predictive cues that are not readily apparent to human practitioners. These systems further optimize treatment plans and decrease diagnostic error rates. Despite these promising findings, there are still many hurdles before AI/ML can hit clinical primetime. Specific limits mainly concern data privacy, the need for extensive validation studies, and the demand for interdisciplinary work. Beyond heralding the transformative power of AI and ML in cardiovascular medicine, this study calls for a comprehensive approach that considers the legal, ethical, and logistical barriers to tipping the balance in favor of implementing these technologies to enhance patient care. Because cardiovascular diseases (CVDs) continue to be a major cause of death globally, new strategies for better diagnosis, care, and treatment are required. Both machine learning (ML) and artificial intelligence (AI) have become potent instruments for improving clinical decision-making, tailoring treatment, and forecasting the course of diseases. The application of AI and ML in CVDs is examined in this study, with particular attention paid to real-time monitoring via wearable technology, risk stratification using predictive models, and early identification with sophisticated imaging. ML-based drug discovery speeds up the identification of new therapeutic targets, while AI-driven algorithms allow for increased accuracy in the diagnosis of arrhythmias, heart failure, and myocardial infarction. Problems are also covered, such as algorithm openness and data privacy. The use of AI and ML has enormous potential to transform cardiovascular treatment and enhance patient outcomes.