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<p>This review explores the integration of emotional artificial intelligence (AI), Internet of Things (IoT), and robotics as converging technologies for advancing patient-centred healthcare, particularly in low- and middle-income countries (LMICs). Drawing from recent literature and supported by two illustrative design prototypes, the paper examines how IoT facilitates real-time physiological monitoring, robotics enhances patient engagement and mobility, and emotional AI introduces empathy-driven responses through multimodal data interpretation. While these technologies offer substantial potential for scalable and responsive care, challenges persist—including data privacy, algorithmic bias, interoperability constraints, and affordability in under-resourced settings. Through a conceptual synthesis of emerging approaches and ethical considerations, this review identifies critical gaps in current research and proposes an integrative framework to support holistic and inclusive digital health systems. The findings highlight the importance of bias mitigation, culturally adaptive emotion recognition, and regulatory alignment in shaping the next generation of AI-enabled healthcare infrastructure.</p>

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Integrating emotional AI, IoT, and robotics for patient-centered healthcare: challenges and future directions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月7日

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  • Nassim Bout,
  • Ghizlane Moukhliss,
  • Hicham Belhadaoui,
  • Nadia Afifi,
  • Mounia Abik

摘要

This review explores the integration of emotional artificial intelligence (AI), Internet of Things (IoT), and robotics as converging technologies for advancing patient-centred healthcare, particularly in low- and middle-income countries (LMICs). Drawing from recent literature and supported by two illustrative design prototypes, the paper examines how IoT facilitates real-time physiological monitoring, robotics enhances patient engagement and mobility, and emotional AI introduces empathy-driven responses through multimodal data interpretation. While these technologies offer substantial potential for scalable and responsive care, challenges persist—including data privacy, algorithmic bias, interoperability constraints, and affordability in under-resourced settings. Through a conceptual synthesis of emerging approaches and ethical considerations, this review identifies critical gaps in current research and proposes an integrative framework to support holistic and inclusive digital health systems. The findings highlight the importance of bias mitigation, culturally adaptive emotion recognition, and regulatory alignment in shaping the next generation of AI-enabled healthcare infrastructure.