Artificial Intelligence for Hypertension Risk Prediction and Management: A Systematic Review
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.