Aug 2026· Trends in cardiovascular medicine· 0 citations· 58 references
Medicine
TL;DR
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.
Abstract
Cardiovascular disease (CVD) remains the leading global cause of mortality, with timely diagnosis and precise risk stratification serving as cornerstones of effective management. Traditional cardiovascular imaging and risk assessment have long been constrained by operator-dependent interpretation, time-intensive manual quantification, and a reliance on static, population-derived diagnostic thresholds. The rapid maturation of artificial intelligence (AI), particularly deep learning (DL), foundation models (FMs), and multimodal integration (MI), has catalyzed a paradigm shift toward automated, quantitative, and patient-specific cardiovascular evaluation. Between 2023 and 2026, AI-driven tools have progressed from retrospective proof-of-concept studies to early prospective clinical validations across echocardiography (EchoCG), cardiac magnetic resonance (CMR), coronary computed tomography angiography (CCTA), and nuclear imaging. Concurrently, AI-enhanced electrocardiography (ECG) and polygenic risk integration have enabled dynamic, longitudinal risk prediction models that outperform conventional scores. Despite these advances, clinical adoption faces substantial hurdles including algorithmic bias, limited generalizability across diverse populations, regulatory fragmentation, workflow integration challenges, and unresolved questions regarding clinical utility and cost-effectiveness. This review critically examines the technological evolution of AI in cardiovascular imaging, evaluates modality-specific applications and emerging digital biomarkers, appraises regulatory and implementation landscapes, and outlines priority research directions. We emphasize that successful integration of AI into cardiovascular care requires rigorous prospective validation, transparent algorithmic governance, equitable data representation, and human-AI collaborative frameworks. As cardiovascular medicine enters the era of precision diagnostics, AI will increasingly serve as a powerful augmentative partner in imaging interpretation, risk stratification, and therapeutic decision-making, provided its meaningful clinical implication is demonstrated through improved patient outcomes.
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.
Simona Giubilato, Lucio Giuseppe Granata, S. Petrina· Giornale italiano di cardiol...· 0 citations
The role of AI-enhanced cardiovascular ultrasound in the transition from descriptive imaging toward predictive and personalized medicine is examined, with AI-enhanced cardiovascular ultrasound poised to become a central tool of precision cardiology.
Ancuța Elena Țupu, Simona Steliana Tudor, C. Dumitru et al.· Journal of Clinical Medicine· 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
Artificial intelligence (AI) is rapidly transforming cardiovascular medicine, driven by the increasing availability of large-scale clinical data and advances in machine learning. Early computational applications in cardiology were primarily limited to rule-based electrocardiogram interpretation systems. Over time, these approaches have evolved into sophisticated deep learning models capable of analysing complex cardiovascular signals and imaging data. In parallel with the broader development of digital health technologies, including wearable devices, electronic health records, and remote monitoring systems, AI applications have expanded across multiple domains of cardiovascular care. These now include electrocardiographic (ECG) and electrophysiological analysis, cardiovascular imaging, surgical planning, and multimodal risk prediction. More recently, multimodal AI models have emerged that integrate heterogeneous data sources such as imaging, physiological signals, clinical records, and genomic information, enabling more comprehensive characterisation of cardiovascular disease. Beyond diagnostic applications, AI is increasingly influencing system-level aspects of cardiovascular medicine, including clinical decision support, workflow optimisation, medical education, and clinical trial design. This narrative review traces the historical and clinical evolution of artificial intelligence in cardiovascular medicine from early automated ECG interpretation systems to contemporary multimodal and system-level applications. It highlights key technological developments, current clinical applications, translational challenges, and the emerging role of AI within digital cardiovascular health ecosystems, with particular emphasis on early disease detection, risk stratification, prognostic modelling, and personalised cardiovascular care.
Overall, AI-empowered echocardiography holds substantial promise for advancing precision diagnosis, risk stratification, and personalized management of HCM, facilitating a transition toward more intelligent and individualized cardiovascular care.
Miao Zhang, Shan-Shan Yuan, Hong-Yan Dai et al.· Frontiers in Cardiovascular...· 0 citations
Pulmonary hypertension (PH) is a heterogeneous clinical syndrome in which similar haemodynamic abnormalities may arise from distinct vascular, cardiac, pulmonary, thromboembolic, and molecular mechanisms. This complexity limits the ability of conventional classifications and risk scores to fully capture individual disease trajectories and treatment responses. Artificial intelligence (AI) offers a framework for integrating clinical data, electrocardiography, multimodal imaging, invasive haemodynamics, biomarkers, and multi-omics information across the PH care pathway. This review summarises current applications of machine learning and deep learning in early detection, diagnostic referral, right-ventricular and pulmonary vascular phenotyping, molecular endotyping, risk stratification, and therapeutic decision support. Available studies show promising results for AI-assisted electrocardiographic screening, automated echocardiographic and cardiac magnetic resonance analysis, computed tomography (CT)-based phenotyping, and multimodal prognostic modelling. True multi-omics integration in PH remains limited to discovery studies and has not yet yielded externally validated endotype or treatment-response classifiers. Evidence maturity is task-dependent: screening and phenotyping span several PH groups, whereas validated risk tools, molecular endotyping, and pathway-directed therapy remain predominantly PAH-based, particularly in idiopathic/heritable PAH. However, most evidence remains retrospective, derives from selected referral populations, and lacks robust external or prospective validation. No AI-based model currently supports routine drug selection or autonomous clinical decision-making. Future progress will require harmonised multicentre datasets and standardised acquisition protocols, transparent and interpretable models, and prospective studies demonstrating meaningful clinical benefit. AI should therefore be viewed as an emerging decision-support tool that may strengthen precision medicine in PH while complementing clinical expertise across diagnosis, phenotyping, risk assessment, and therapeutic stratification pathways.
Sergio Ferrantelli, Alessandro Del Cuore, G. Cassataro et al.· International Journal of Mol...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
