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Cardiovascular Disease and Adiposity / Artificial Intelligence in Healthcare and Education · Journal article
Giornale Italiano Di Cardiologia · August 26, 2026
A consensus or society position rather than new primary data.
This is a narrative review examining AI and machine/deep learning applications for cardiovascular risk stratification, early disease detection, and clinical integration in prevention. The authors identify AI as a potential tool to bridge the gap between guideline recommendations and clinical practice, while emphasizing that translation to routine practice requires prospective validation, RCTs, and heterogeneous population testing.
Journal article. Patients requiring cardiovascular risk assessment and prevention in clinical settings; Italian context emphasized. Italy (context cited).
AI-enabled electrocardiography may support early detection of subclinical atrial fibrillation, left ventricular dysfunction, and coronary artery disease Machine learning and deep learning models enable more precise, dynamic, and personalized cardiovascular risk stratification compared with traditional risk scores Cardiovascular prevention requires integration of digital biomarkers, genetic data, and wearable device data alongside AI models
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
Clinicians should recognize AI as an emerging strategy to improve risk prediction and early detection in cardiovascular prevention, but recognize that robust prospective validation and integration into clinical workflows remain necessary before widespread adoption. Current practice should remain grounded in guideline-based risk scores until AI models are validated in diverse populations.
A narrative review synthesizing current evidence on AI applications in cardiovascular prevention, addressing implementation gaps and future requirements rather than reporting original trial results.
As stated by the source record.
Clinicians should recognize AI as an emerging strategy to improve risk prediction and early detection in cardiovascular prevention, but recognize that robust prospective validation and integration into clinical workflows remain necessary before widespread adoption. Current practice should remain grounded in guideline-based risk scores until AI models are validated in diverse populations.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
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.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.