The rapid convergence of multimodal artificial intelligence (AI) and telemedicine is transforming the landscape of remote health monitoring, offering unprecedented opportunities to extend high-quality, continuous, and patient-centered care beyond traditional clinical environments. Telemedicine, accelerated globally during the COVID-19 pandemic, has evolved into a critical component of modern health systems, enabling remote access, reducing facility burden, and supporting continuity of care during crises. The integration of AI enhances this model by enabling intelligent triage, automated monitoring, adaptive decision support, and personalized interventions. Multimodal AI systems—capable of synthesizing diverse inputs such as biometric signals, clinical text, speech, video consultations, and patient-reported outcomes—mimic human clinical reasoning and enable more accurate assessment in settings where diagnostic resources are limited. This chapter provides a comprehensive examination of the conceptual foundations, global use cases, and system architecture of multimodal AI-enabled telemedicine. It highlights how multimodal integration enhances chronic disease management, improves early warning capabilities, strengthens outbreak response, and promotes health equity in remote, rural, and underserved populations. Drawing on theoretical frameworks such as TAM (Technology Acceptance Model), UTAUT (Unified Theory of Acceptance and Use of Technology), Delphi Method, PREMs (Patient-Reported Experience Measures), and implementation science models, the chapter outlines user-centered approaches for designing and scaling AI-enhanced telehealth systems. A detailed case study from Malaysia’s SELangkah Virtual COVID-19 Assessment Centre demonstrates real-world feasibility, identifying improvements in patient monitoring alongside challenges related to digital literacy, user engagement, and hidden out-of-pocket costs. The chapter also critically discusses ethical, regulatory, and equity considerations—including informed consent, algorithmic bias, data privacy, accountability, and digital inclusion—highlighting the need for strong governance and contextual adaptation. Ultimately, multimodal AI has immense potential to strengthen remote health ecosystems, but its success requires responsible implementation, robust policy support, and sustained investment in digital infrastructure, workforce capacity, and equitable access. When effectively integrated, it represents a powerful pathway toward intelligent, inclusive, and resilient healthcare systems of the future.

Multimodal AI and Telemedicine in Remote Health Monitoring

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发表日期：

2026年

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Kalaivane Kannadasan

摘要

The rapid convergence of multimodal artificial intelligence (AI) and telemedicine is transforming the landscape of remote health monitoring, offering unprecedented opportunities to extend high-quality, continuous, and patient-centered care beyond traditional clinical environments. Telemedicine, accelerated globally during the COVID-19 pandemic, has evolved into a critical component of modern health systems, enabling remote access, reducing facility burden, and supporting continuity of care during crises. The integration of AI enhances this model by enabling intelligent triage, automated monitoring, adaptive decision support, and personalized interventions. Multimodal AI systems—capable of synthesizing diverse inputs such as biometric signals, clinical text, speech, video consultations, and patient-reported outcomes—mimic human clinical reasoning and enable more accurate assessment in settings where diagnostic resources are limited. This chapter provides a comprehensive examination of the conceptual foundations, global use cases, and system architecture of multimodal AI-enabled telemedicine. It highlights how multimodal integration enhances chronic disease management, improves early warning capabilities, strengthens outbreak response, and promotes health equity in remote, rural, and underserved populations. Drawing on theoretical frameworks such as TAM (Technology Acceptance Model), UTAUT (Unified Theory of Acceptance and Use of Technology), Delphi Method, PREMs (Patient-Reported Experience Measures), and implementation science models, the chapter outlines user-centered approaches for designing and scaling AI-enhanced telehealth systems. A detailed case study from Malaysia’s SELangkah Virtual COVID-19 Assessment Centre demonstrates real-world feasibility, identifying improvements in patient monitoring alongside challenges related to digital literacy, user engagement, and hidden out-of-pocket costs. The chapter also critically discusses ethical, regulatory, and equity considerations—including informed consent, algorithmic bias, data privacy, accountability, and digital inclusion—highlighting the need for strong governance and contextual adaptation. Ultimately, multimodal AI has immense potential to strengthen remote health ecosystems, but its success requires responsible implementation, robust policy support, and sustained investment in digital infrastructure, workforce capacity, and equitable access. When effectively integrated, it represents a powerful pathway toward intelligent, inclusive, and resilient healthcare systems of the future.