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Full Description
The book provides comprehensive research ideas about Edge-AI technology that can assist doctors in making better data-driven decisions. It provides insights for improving the healthcare industry by examining future trends, simplifying decision making and investigating structured and unstructured data.
Edge-AI in Healthcare: Trends and Future Perspective is more than a comprehensive introduction to Artificial Intelligence as a tool in healthcare data. The book is split into five chapters covering the entire healthcare ecosystem. First section is introduction to Edge-AI in healthcare. It discusses data usage, modelling and simulation techniques as well as machine and deep learning approaches. The second section discusses the implementation of edge AI for smart healthcare. The topics discussed in this section include, AR/VR and cloud computing, big data management, algorithms, optimization, and IoMT techniques and methods. Third section covers role of Edge-AI in healthcare and the challenges and opportunities of the technologies. This section also provides case studies and discusses sustainability, security, privacy, and trust related to Edge-AI in healthcare.
This book is intended to benefit researchers, academics, industry professionals, R & D organizations and students working in the field of healthcare, healthcare informatics and their applications.
Contents
1 Introduction to edge-AI in healthcare 2 Edge-AI tools and techniques for healthcare 3 Edge-AI, Machine-Learning, and Deep-Learning Approaches for Healthcare 4 Transforming healthcare with machine-learning and deeplearning approaches 5 Enhancing access of the visually impaired through the smart cane 6 Contemporary role of edge-AI in IoT and IoE in healthcare and digital marketing 7 Authentication of edge-AI-based smart healthcare system: A review-based study 8 Automated Wheelchair for the Physically Challenged with AIoT Modules
9 Comparison of machine-learning and deep-learning algorithms for stroke prediction 10 Edge computing-based containerized deep-learning approach for intrusion detection in healthcare IoT 11 Human mental experience through chatbots: A thematical analysis of human engagement with evidence-based cognitive-behavioral techniques 12 An early diagnosis of cardiac disease using featureoptimization-based deep neural network 13 Super-resolution in a world of scarce resources for medical imaging applications 14 Legal and ethical implications of edge-AI-enabled IoT healthcare monitoring systems 15 The prospective role of artificial intelligence in the development dynamic of healthcare sectors 16 Edge-AI-empowered blockchain: A game-changer for the medical tourism industry 17 System for secure edge healthcare monitoring based on artificial intelligence
