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.
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.
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
A scoping review of AI applications in predicting clinical outcomes related to angiographic procedures, including diagnosis, risk stratification, and postprocedural monitoring finds promising capabilities in outcome prediction within coronary angiography.
S. Tulpule, S. Jadhav· Journal of Cardiovascular Im...· 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 interv...
Simona Giubilato, L. Granata, S. Petrina· Giornale italiano di cardiol...· 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.
Rui-Feng Liu, R. Arena, V. Vasile et al.· Progress in cardiovascular d...· 0 citations
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.
S. Pai, R. Subramanian, Lakshmi Krishnan et al.· BMC Medical Informatics and...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
