AI has the potential to substantially transform hypertension management by enabling more precise, proactive, and personalized care but major barriers to implementation include data heterogeneity, algorithmic bias, limited interpretability, insufficient external validation, infrastructure and cost requirements, regulatory uncertainty, and concerns regarding patient trust and data privacy.
Hypertension is one of the Crucial global contributors to cardiovascular diseases and death. It often progresses without any significant symptoms. Conventional care models are bounded by intermittent clinical measurements, therapeutic inertia, and inadequate personalization. Recent advancements in Artificial Intelligence (AI) empower a shift toward proactive, precision‐driven hypertension management. It keeps inspection, early risk detection, and intelligent clinical decision support. This systematic review analyses the effectiveness of integrated AI‐based hypertension prediction and management systems those integrate real‐time oversight, interpretable risk prediction, customized lifestyle intervention, clinical decision support, and early warning procedure. Following the PRISMA 2020 guidelines, 40 peer‐reviewed studies those were published between 2020 and 2026 were systematically reviewed across prominent healthcare and AI databases. AI‐driven systems validated strong predictive performance like high AI accuracy of Area Under the Curve (AUC) up to 0.97 and improved early detection and clinical actionability, but results are hard to compare due to differences in data and methods. Integrated AI‐based systems demonstrate strong potential to transform hypertension care, though long‐term validation remains limited.
Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide, necessitating improved approaches for early detection and risk stratification. Traditional cardiovascular risk assessment models often demonstrate limited predictive accuracy because they rely on a restricted number of clinical variables and linear statistical relationships. Artificial intelligence (AI)-driven predictive analytics has emerged as a promising solution capable of processing large, complex, and multidimensional healthcare datasets to improve cardiovascular risk prediction and clinical decision-making. This systematic review aimed to evaluate the effectiveness of AI-based predictive models for the early detection and risk stratification of CVD. A comprehensive literature search was conducted across PubMed/MEDLINE, Scopus, Web of Science, Embase, CINAHL, IEEE Xplore, and the Cochrane Library for studies published between January 2015 and December 2025. A total of 130 studies involving approximately 12.8 million participants met the eligibility criteria and were included in the review. The findings 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. The strongest evidence was observed in the prediction of coronary artery disease, heart failure, atrial fibrillation, stroke, and major adverse cardiovascular events. Electronic health records, electrocardiography, medical imaging, wearable devices, and multimodal datasets were the most commonly utilized data sources. Despite their promising performance, challenges related to external validation, interpretability, algorithmic bias, and clinical implementation remain.
N. Muruganandan, P. Prathiba, Khyati Rajeshkumar Patel et al.· International Journal of Res...· 0 citations
Artificial intelligence (AI) is emerging as a transformative tool in cardiovascular imaging, particularly in coronary angiography. With the growing interest in precision medicine, AI offers the potential to enhance clinical decision-making by improving diagnostic accuracy, predicting outcomes, and guiding interventions. This scoping review aims to examine the current landscape of AI applications in predicting clinical outcomes related to angiographic procedures, including diagnosis, risk stratification, and postprocedural monitoring. A total of 59 relevant studies published between 2015 and 2025 were included in this review. The selection was based on predefined inclusion criteria focused on AI-based models applied in coronary artery disease diagnosis, outcome prediction and restenosis detection. The studies were reviewed for the AI techniques used, clinical endpoints targeted, model performance, validation strategies, and limitations. Four primary themes were identified: (1) prediction of adverse cardiovascular events such as myocardial infarction and mortality; (2) detection of hemodynamically significant stenosis; (3) prediction of in-stent restenosis; and (4) identification of coronary arteries. Machine learning algorithms, especially random forests, support vector machines, and convolutional neural networks, have been commonly used. Many models have demonstrated superior performance compared to traditional statistical methods. However, limitations such as small sample sizes, lack of external validation, retrospective designs, and concerns about data privacy were frequently observed in these studies. AI demonstrates promising capabilities in outcome prediction within coronary angiography. Despite current limitations, its integration into clinical practice is feasible with prospective, multicentric studies and standardized validation frameworks. Ethical considerations and regulatory oversight will be crucial in ensuring safe and effective implementation.
Shreeyash Tulpule, S. Jadhav· Journal of Cardiovascular Im...· 0 citations
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, Salvatore Massimo Petrina· Giornale italiano di cardiol...· 0 citations
Since 2020, artificial intelligence (AI) has been increasingly applied to atherosclerotic cardiovascular disease (ASCVD) risk prediction. This structured narrative review with systematic evidence mapping summarizes literature (2020-2026) examining study design, data sources, model architectures, multimodal fusion, model development and validation, performance evaluation, subgroup applications, and implementation barriers. Overall, 126 studies informed the review; 93 provided sufficient information for structured extraction, including prevention setting, exact input variables, comparator scores, validation strategies, discrimination, calibration, dominant model architecture, endpoint category, foundation-model or pretrained-model status, regulatory status, and implementation features. AI-based models may offer modest but clinically meaningful gains over conventional risk equations, especially with multimodal or longitudinal data. Among the 93 studies, traditional machine learning accounted for 83 (89%), deep learning for 7 (8%), and multimodal fusion for 3 (3%). Endpoint definitions were heterogeneous (23% ASCVD-specific; 63% expanded MACE composites). Among these studies, no large language model or federated learning was used for risk prediction. Appropriate comparators should now include contemporary equations such as PREVENT, rather than only legacy tools. Major barriers remain, including limited external validation, performance attenuation, data and algorithmic bias, limited interpretability, inconsistent reporting of calibration, fairness, and clinical utility, unclear regulatory status, and limited prospective evidence. Future work should prioritize calibration, robustness, transportability, fairness, interpretability, regulatory clarity, workflow integration, and prospective evidence of decision impact or post-deployment benefit. The central question is not only whether AI can detect complex patterns, but whether such models can be trusted, implemented, and shown to advance preventive cardiology in real-world settings.
Ruifeng Liu, Ross Arena, V. Vasile et al.· Progress in cardiovascular d...· 0 citations
BACKGROUND
Cardiovascular disease (CVD) is a leading cause of death worldwide, making early risk prediction essential for improving outcomes. Although artificial intelligence (AI) models promise to improve predictions, questions remain about interpretability, the reliability of risk factors, and the need for cross-validation. This scoping review examined the extent, types, and reporting quality of studies that used AI-based prognostic models to predict CVD risk in individuals without established CVD.
METHODS
A systematic search of PubMed, IEEE Xplore, Web of Science, Scopus, and Google Scholar identified 6,710 records. Screening and data extraction followed the PRISMA-ScR framework and JBI Guidelines for Scoping Reviews. Included studies were evaluated against the TRIPOD-AI reporting checklist. The review protocol was registered with the Open Science Framework (OSF: https://osf.io/8nq6s/).
RESULTS
Thirty studies met the inclusion criteria; all published after 2017. Most studies utilized existing models on large datasets, predominantly leveraging unimodal clinical data and established machine learning algorithms such as Random Forests and Support Vector Machines. Twenty-four of the 30 studies used unimodal approaches, and the six multimodal studies demonstrated consistently strong performance, but they rest on a small, heterogeneous set of studies. Twelve studies conducted direct comparisons with traditional risk scores such as the Framingham Risk Score, showing comparable or modestly improved discrimination, although methodological heterogeneity limits the strength of these conclusions. External validation was reported in only seven studies, and calibration (agreement between the predicted and observed event rates) was not reported in any of the 30 included studies. Sensitivity, which determines a model's ability to identify truly high-risk individuals, was the least reported metric, appearing in only four studies, and no study reported a decision curve analysis.
CONCLUSION
AI-based CVD risk prediction tools show promise but have critical gaps in validation, calibration reporting, and clinical utility assessment, which currently preclude clinical deployment. Future research should prioritize external validation across diverse populations, mandatory reporting of calibration and sensitivity alongside discrimination metrics, as well as the adoption of reporting standards and appraisal tools such as Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis + Artificial Intelligence (TRIPOD + AI) or Prediction Model Risk of Bias Assessment Tool + Artificial Intelligence (PROBAST + AI) to improve transparency and reproducibility.
S. Pai, Rekha Subramanian, Lakshmi Krishnan et al.· BMC Medical Informatics and...· 0 citations