Aug 2026· Engineering Reports· Vol 8· 0 citations· 41 references
TL;DR
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.
Abstract
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.
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
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, driving the need for reliable tools for early risk prediction. Artificial intelligence (AI) applied to electrocardiograms (ECGs) has shown strong predictive performance for future cardiac events, yet its clinical adoption remains limited by the lack of transparency and trust associated with black-box models. This systematic review examines recent advances in AI-based cardiovascular prediction, focusing on the combined challenges of multi-modal data fusion and clinically actionable explainability. Following PRISMA 2020 guidelines, we analyzed 65 peer-reviewed studies published between 2018 and 2025, identified through a systematic search of PubMed, IEEE Xplore, Web of Science, Scopus, ACM Digital Library, and Google Scholar. The reviewed literature reveals that while most AI-ECG models achieve high predictive accuracy, typically AUC 0.85–0.95, the majority rely on post hoc explainability techniques that offer limited clinical insight, and 61.5% of included studies implement no explainability method at all. External validation remains critically underutilized, performed by only 12.3% of studies, and multi-modal approaches integrating ECG data with electronic health records, biomarkers, or genomics represent only 27.7% of the reviewed literature. While these multi-modal models demonstrate improved contextualization and predictive performance, they remain insufficiently validated and inconsistently interpretable. Among studies employing XAI techniques, attention mechanisms were the most prevalent approach (28% of XAI studies), followed by saliency maps (20%), SHAP (16%), and LIME (8%). Only 9.2% of studies were prospective or clinical trials, underscoring the gap between algorithmic development and real-world clinical deployment. Applying a pre-specified four-level clinical actionability scoring framework (Level 0–3), we found that the majority of studies (61.5%) scored at Level 0 (no actionability), with only 9.2% reaching Level 3 (demonstrated clinical impact), confirming that the clinical translation gap extends beyond trial design to encompass the broader absence of clinically contextualised evaluation of AI-ECG systems. This review highlights a persistent and critical gap between predictive performance and clinical usability, and outlines four key directions for developing AI-ECG systems that can better support trustworthy clinical decision-making: (1) developing inherently interpretable architectures, (2) advancing unified multi-modal fusion and explanation frameworks, (3) establishing standardized benchmarks for explainability evaluation, and (4) conducting robust prospective validation measuring real-world patient outcomes.
Hamza Nouri, Rafae Abderrahim, Mohamed Erritali· BioMedInformatics· 0 citations
Future work should prioritize calibration, robustness, transportability, fairness, interpretability, regulatory clarity, workflow integration, and prospective evidence of decision impact or post-deployment benefit, as well as major barriers remain.
Ruifeng Liu, Ross Arena, V. Vasile et al.· Progress in cardiovascular d...· 0 citations
This systematic review examines contemporary machine learning methods and explainable AI procedures engaged prediction of heart diseases and classification and applies explainability AI heart disease prediction models that will ease the process and also make the upcoming system with better progress and more trustworthy, operative and unfailing clinical solutions that makes system decision support one.
R. Jain, Sachin H. Patel· International journal of com...· 0 citations
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.
Simona Giubilato, L. Granata, S. Petrina· Giornale italiano di cardiol...· 0 citations
Background Stroke remains a leading cause of mortality, long-term disability, and healthcare expenditure worldwide, placing substantial strain on healthcare systems, particularly in low- and middle-income countries. Effective risk stratification can facilitate targeted prevention strategies, optimize resource allocation, and reduce avoidable hospitalizations. This study synthesizes existing evidence on the predictive performance of artificial intelligence (AI)-based models for stroke risk assessment through meta-analysis and explores their potential implications for healthcare system planning. Methods Studies were systematically retrieved from Web of Science (WoS), PubMed, and Scopus until 31 January 2025. The review followed the PRISMA 2020 guidelines. Area Under the Receiver Operating Characteristic Curve (AUC) values were extracted for each algorithm type and pooled using meta-analytic methods. Results Deep learning (DL) algorithms demonstrated favorable pooled discriminative performance (AUC: 0.955; 95% CI: 0.906–1.00, I2 = 85.75%), especially for imaging-based models. Sensitivity analysis modestly reduced heterogeneity (I2 from 85.75% to 61.77%). Substantial heterogeneity remained across study populations, healthcare settings, predictor characteristics, and validation strategies, limiting the generalizability of findings. Conclusions AI-based models, particularly DL approaches, demonstrate favorable predictive performance for stroke risk stratification. However, considerable methodological heterogeneity, limited external validation, and risk of bias reduce confidence in widespread clinical implementation. Future research should follow standardized reporting and validation frameworks, such as TRIPOD, to improve methodological rigor, transparency, and clinical applicability.
Raoof Nopour· Inquiry : a journal of medic...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
