Artificial Intelligence and Precision Medicine in Cardiovascular Disease Prediction: A Technical Review
Keywords:
Artificial Intelligence, Cardiovascular Disease Prediction, Precision Medicine, Machine Learning, Deep Learning, Biomarkers, Polygenic Risk Scores, Digital Health, Electrocardiography, Risk StratificationAbstract
Cardiovascular disease (CVD) remains the foremost cause of global morbidity and mortality, responsible for approximately 17.9 million deaths annually. Despite advances in pharmacological therapy and interventional cardiology, traditional risk stratification tools have demonstrated limited capacity to identify high-risk individuals before clinical events occur. Precision medicine, powered by artificial intelligence (AI), offers a transformative paradigm capable of integrating multimodal biological, clinical, and environmental data into individualized predictive frameworks. A comprehensive narrative review of peer-reviewed literature published between 2021 and 2026 was conducted using PubMed/MEDLINE, Scopus, and Web of Science. Reviewed evidence demonstrates that AI-driven algorithms including deep learning, gradient boosting, and federated learning frameworks significantly outperform conventional scoring systems in CVD risk stratification. Integration of genomic data, wearable-derived biomarkers, imaging phenotypes, and electronic health record mining enables real-time, individualized risk prediction with improved sensitivity and specificity. Key limitations include algorithmic bias, data governance challenges, and limited generalizability across diverse populations. The convergence of AI and precision medicine represents a clinically significant advancement in cardiovascular risk management. Standardized implementation frameworks, equitable algorithm development, and prospective validation are essential for translating these technologies into routine clinical practice.
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Copyright (c) 2026 Jeremy Mytskevych (Author)
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