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With hypertension (HTN) affecting over one billion people globally, there is an urgent need for innovative strategies to improve its detection, treatment, and control. Our paper investigates the transformative role of Artificial Intelligence (AI) and Internet of Things (IoT) technologies in smart HTN management. It examines how wearable devices, mobile health applications, AI-powered decision support systems, and telemonitoring tools are being leveraged to personalise care, enhance adherence, and enable real-time blood pressure tracking. Our work also explores the supporting infrastructure, including edge computing and interoperability standards, alongside challenges such as data privacy, algorithmic bias, and clinical validation. Special attention is given to low- and middle-income countries, where digital health innovations—especially those involving community health workers—offer promising solutions for addressing systemic gaps. Finally, the review outlines future directions focused on explainable AI, multimodal data integration, and equitable implementation to advance personalised and inclusive HTN care worldwide.

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Artificial Intelligence and the Internet of Things in Smart Hypertension Management: A State-of-the-Art Review and Future Directions

  • 会议论文

  • 发表日期：

    2026年

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  • Sara Ramdani,
  • Nour El Houda Benkaddour,
  • Ismail Chergui,
  • Intissar Haddiya

摘要

With hypertension (HTN) affecting over one billion people globally, there is an urgent need for innovative strategies to improve its detection, treatment, and control. Our paper investigates the transformative role of Artificial Intelligence (AI) and Internet of Things (IoT) technologies in smart HTN management. It examines how wearable devices, mobile health applications, AI-powered decision support systems, and telemonitoring tools are being leveraged to personalise care, enhance adherence, and enable real-time blood pressure tracking. Our work also explores the supporting infrastructure, including edge computing and interoperability standards, alongside challenges such as data privacy, algorithmic bias, and clinical validation. Special attention is given to low- and middle-income countries, where digital health innovations—especially those involving community health workers—offer promising solutions for addressing systemic gaps. Finally, the review outlines future directions focused on explainable AI, multimodal data integration, and equitable implementation to advance personalised and inclusive HTN care worldwide.