Publicação
Edge Intelligence in Healthcare - Systematic Literature Review
| Resumo: | This dissertation explores the integration of Edge Intelligence (EI) in healthcare through a comprehensive two-part analysis. Chapter I conducts a Systematic Literature Review to examine how EI—by combining artificial intelligence with edge computing—enhances healthcare delivery. The review, based on PRISMA guidelines, evaluates 24 high-quality articles, identifying how EI reduces latency, supports real-time decision-making, and strengthens data privacy. It also highlights technical limitations such as device resource constraints, lack of standardization, and security risks. The chapter culminates in a conceptual model that illustrates how EI, IoMT (Internet of Medical Things), and supporting technologies interact to create more efficient and personalized healthcare systems. Chapter II expands the scope by specifically focusing on the integration of EI and IoMT for real-time healthcare analytics. Using another systematic review, this chapter analyzes 32 peer-reviewed studies to assess how IoMT devices—such as sensors and wearables—work with edge computing to enable continuous patient monitoring and early intervention. It examines key enablers like Federated Learning and blockchain, which address privacy and data sharing challenges. The analysis also underscores the importance of interoperability, communication protocols, and data synchronization in heterogeneous healthcare systems. Together, the chapters offer a holistic view of EI's potential and practical impact on smart healthcare. The dissertation concludes by recommending future research into lightweight AI models, ethical governance, and secure, interoperable architectures to fully leverage EI in healthcare innovation. |
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| Autores principais: | Sara Oliveira Marques |
| Assunto: | Edge Intelligence Edge Computing Healthcare Systems Integration Internet of Medical Things Real-time Analytics Systematic Literature Review |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Superior de Gestão e Administração de Santarém |
| Idioma: | inglês |
| Origem: | Instituto Superior de Gestão e Administração de Santarém |
| Resumo: | This dissertation explores the integration of Edge Intelligence (EI) in healthcare through a comprehensive two-part analysis. Chapter I conducts a Systematic Literature Review to examine how EI—by combining artificial intelligence with edge computing—enhances healthcare delivery. The review, based on PRISMA guidelines, evaluates 24 high-quality articles, identifying how EI reduces latency, supports real-time decision-making, and strengthens data privacy. It also highlights technical limitations such as device resource constraints, lack of standardization, and security risks. The chapter culminates in a conceptual model that illustrates how EI, IoMT (Internet of Medical Things), and supporting technologies interact to create more efficient and personalized healthcare systems. Chapter II expands the scope by specifically focusing on the integration of EI and IoMT for real-time healthcare analytics. Using another systematic review, this chapter analyzes 32 peer-reviewed studies to assess how IoMT devices—such as sensors and wearables—work with edge computing to enable continuous patient monitoring and early intervention. It examines key enablers like Federated Learning and blockchain, which address privacy and data sharing challenges. The analysis also underscores the importance of interoperability, communication protocols, and data synchronization in heterogeneous healthcare systems. Together, the chapters offer a holistic view of EI's potential and practical impact on smart healthcare. The dissertation concludes by recommending future research into lightweight AI models, ethical governance, and secure, interoperable architectures to fully leverage EI in healthcare innovation. |
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