Sep 2026· Giornale italiano di cardiologia· Vol 27 9, pp.
604-614
· 0 citations
Medicine
TL;DR
Overall, AI outlines the transition from a reactive cardiology model toward a predictive, proactive, and precision-based approach in cardiovascular prevention, with a specific focus on risk stratification, early detection of subclinical disease, and identification of patients most likely to benefit from targeted interventions.
Abstract
Cardiovascular diseases remain the leading cause of mortality and morbidity worldwide, with substantial impact in Italy. Cardiovascular prevention is a strategic priority, yet a significant gap persists between evidence-based guideline recommendations and their actual implementation in clinical practice. Artificial intelligence (AI), through machine learning and deep learning models, is emerging as a potentially transformative technology to bridge this gap, enabling more precise, dynamic, and personalized cardiovascular risk stratification compared with traditional risk scores. This review examines the most recent evidence on the application of AI in cardiovascular prevention, with a specific focus on risk stratification, early detection of subclinical disease, and identification of patients most likely to benefit from targeted interventions. It addresses the limitations of conventional risk scores and the contribution of emerging risk determinants, including digital biomarkers, genetic data, and wearable devices. It discusses the role of AI-enabled electrocardiography in the early detection of subclinical atrial fibrillation, left ventricular dysfunction, and coronary artery disease; the potential of opportunistic imaging (chest radiography, chest and coronary computed tomography, mammography) for subclinical atherosclerosis; and the integration of AI into clinical care pathways, electronic health records, clinical decision support systems, and telemonitoring networks. Overall, AI outlines the transition from a reactive cardiology model toward a predictive, proactive, and precision-based approach. Translation into routine clinical practice requires robust prospective evidence, randomized controlled trials, validation in heterogeneous populations, improved model interpretability, and adequate digital and regulatory infrastructures.
It is emphasized that successful integration of AI into cardiovascular care requires rigorous prospective validation, transparent algorithmic governance, equitable data representation, and human-AI collaborative frameworks, provided its meaningful clinical implication is demonstrated through improved patient outcomes.
Xu Xia, Wasim Ullah Khan, Q. Khan et al.· Trends in cardiovascular med...· 0 citations
It was demonstrated that machine learning (ML) and deep learning (DL) models consistently outperformed conventional cardiovascular risk prediction tools, achieving area under the receiver operating characteristic curve (AUC) values ranging from 0.80-0.99 across various cardiovascular conditions.
N. Muruganandan, P. Prathiba, Khyati Rajeshkumar Patel et al.· International Journal of Res...· 0 citations
Atherosclerosis remains a leading global cause of cardiovascular morbidity and mortality, yet its insidious progression and multifaceted etiology, spanning genetic, metabolic, and environmental determinants, often delay clinical recognition until adverse events occur. Traditional risk stratification tools, while foundational in preventive cardiology, are constrained by their reliance on limited variables and static linear assumptions, frequently misclassifying individuals at the extremes of risk. This review critically examines the transformative role of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in redefining atherosclerosis management across three interconnected domains. First, we explore how AI-driven predictive models integrate high-dimensional data, from genomics and imaging to real-time wearable metrics, to achieve superior cardiovascular risk stratification compared with conventional scores. Second, we detail AI’s capacity to automate and enhance plaque characterization through advanced imaging analysis, enabling reproducible quantification of burden, composition, and vulnerability markers that are imperceptible to human readers. Third, we investigate AI-powered clinical decision support systems, digital twins, and reinforcement learning approaches that facilitate dynamic, personalized treatment planning tailored to each patient’s evolving profile. We also critically address the ethical imperatives, algorithmic fairness, data privacy, transparency, and accountability, alongside practical challenges of clinical integration, regulatory validation, and health equity.
R. Aipov, T. Saliev, B. Aipov et al.· Journal of Cardiovascular De...· 0 citations
Stable angina pectoris is a prevalent condition with concerning symptoms and an increased risk of myocardial infarction (MI), stroke, heart failure, and mortality. Risk stratification is important in preventive care because the severity of the condition significantly impacts prognosis. With the availability of digital data such as electrocardiogram (ECG) and clinical variables, artificial intelligence (AI) approaches, including machine learning and deep learning, can be beneficial for risk prediction of chronic diseases, such as coronary artery disease (CAD), in patients with stable angina. At the same time, traditional approaches such as Diamond–Forrester, Framingham Risk Score, and PROCAM lag because they rely on linear and population-oriented assumptions. This systematic review, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020, evaluates AI models that have been developed to predict cardiovascular events in adults suffering from a stable angina condition. Out of 1,250 articles, only five research studies have been included because they predict the risk of cardiovascular diseases in patients suffering from stable angina. Data modalities in these studies include longitudinal electronic health record (EHR) variables, patient-reported outcomes (Seattle Angina Questionnaire), invasive angiography indices (e.g., Gensini score), and 12-lead ECG. With area under the curve (AUC) varying from moderate (about 0.78–0.83) to excellent (> 0.95) based on outcome and data richness, AI enhances discrimination for obstructive CAD and adverse outcomes across studies. This review highlights that there are a limited number of studies on this topic and a lack of clinical validity. Also, the final set of features in model development varies highly. Hence, heterogeneity, a lack of external validation, and implementation limitations exist.
Sunil Kumar, Abdullah Abdul Sami, Manish Kumar et al.· Cardiology Research· 0 citations
The use of AI in cardiovascular care is expected to optimize resource allocation, reduce healthcare costs, and ultimately improve survival rates, despite ongoing challenges with data quality, model transparency, and ethical considerations.
Srushti Bhupesh Patil, Y. Patil, K. Patil et al.· Cardiovascular & Haematologi...· 0 citations
Abstract Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, underscoring the need for effective risk prediction and early detection. Although the electrocardiogram (ECG) is a widely available and low-cost diagnostic tool, its traditional interpretation is limited by subjectivity. Artificial intelligence (AI) has emerged as a promising approach, capable of extracting hidden prognostic information from ECG signals. This systematic review aimed to assess original studies applying AI techniques to ECGs for cardiovascular risk prediction and mortality. Original studies that used ECG signals as the sole input variable for AI models, focusing on cardiovascular risk outcomes, were included. A systematic search was conducted in different databases, and data were synthesized narratively. Eleven studies were included, predominantly retrospective cohorts applying convolutional neural networks (CNNs) to predict cardiovascular risk or mortality. The sample primarily consisted of adult populations in high-income countries. Primary outcomes included all-cause mortality, cardiovascular death, and major adverse cardiovascular events (MACE). Reported AUROC values ranged from 0.63 to 0.961 in training sets, with some models outperforming traditional risk scores. AI-ECG models demonstrated the potential to detect subclinical disease, enabling early risk stratification even in normal ECGs. However, challenges remain regarding population diversity, model interpretability, and prospective validation. The application of AI to ECG analysis represents a promising advancement in personalized cardiovascular risk assessment. Nonetheless, further research is needed to ensure the safety, effectiveness, and equitable clinical integration of these technologies.
Maria Clara Mantoan Pinheiro, L. Felberg, I. Bozzi et al.· Arquivos Brasileiros de Card...· 0 citations
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