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Edge Intelligence in Healthcare - Systematic Literature Review
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| 1.57 MB | Adobe PDF |
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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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Edge Intelligence Edge Computing Healthcare Systems Integration Internet of Medical Things Real-time Analytics Systematic Literature Review