 Название: 6G-Enabled IoT and AI for Smart Healthcare: Challenges, Impact, and Analysis
Автор: Ashish Kumar, Rachna Jain, Meenu Gupta
Издательство: CRC Press
Год: 2023
Страниц: 267
Язык: английский
Формат: pdf (true)
Размер: 10.06 MB
In today’s era, there is a need for a system that can automate the process of treatment for the patient if medical facilities are out of reach. Smart healthcare can step in to make the patient more self-dependent. 6G with its features can be seen as the future of smart healthcare with IoT and AI.
6G-Enabled IoT and AI for Smart Healthcare: Challenges, Impact, and Analysis offers the fundamentals, history, reality, and challenges faced in the smart healthcare industry today. It discusses the concepts, tools, and techniques of smart healthcare as well as the analysis used. The book details the role that machine learning-based deep learning and 6G-enabled IoT concepts play in the automation of smart healthcare systems. The book goes on to presents applications of smart healthcare through various real-world examples and includes chapters on security and privacy in the 6G-enabled and IoT environment, as well as research on the future prospects of the smart healthcare industry.
A completely AI-driven communication network will be 6G. Using 6G, every component of network transmission would be smarter, enabling it to make decisions on its own when necessary. With 6G, the entire planet will be covered, which includes air, space, as well as the ocean. This is only possible by “intelligent AI” being created from the various communication components. AI algorithm implementation results in great performance and accuracy in communication networks. Healthcare powered by AI enhances clinical diagnostics and decision-making. AI is needed in the healthcare industry to do jobs instantly. Data preprocessing is not necessary for Deep Learning (DL). Instantaneous data could be used as input because it does the computation using original health data. Additionally, in computing a significant number of network characteristics, it exhibits great accuracy. In a similar vein, other AI system currently being investigated on healthcare data is Deep Reinforcement Learning (DRL).
Reinforcement learning involves the structure first formulating some judgments before seeing the outcomes. Based on observation, the decision is once more computed to produce the optimal result. DRL combines the advantages of deep neural networks and reinforcement learning methods. DRL provides good performance within a short calculation time as a result. Federated AI will also share its information with other intelligent devices, improving healthcare. AI algorithms have performed well. Algorithms for AI demand pricey infrastructure. Proactive caching is another area where AI is recommended. All AI algorithms are quite computationally intensive. The heavy computation task requires more time and energy. However, 6G is unable to offer such comfort. The AI algorithms which would be employed in 6G will have issues. For instance, the neural networks have many layers. To improve the performance of 6G, which increases the efficiency of healthcare, research is being done to improve AI procedures having reduced calculation period and reduced power utilization.
This book:
Offers the fundamentals, history, reality, and the challenges faced in the smart healthcare industry
Discusses the concepts, tools, and techniques of smart healthcare as well as the analysis used
Details the role that machine learning-based deep learning and 6G-enabled IoT concepts play in the automation of smart healthcare systems
Presents applications of smart healthcare through various real-world examples
Includes topics on security and privacy in 6G-enabled IoT, as well as research and future prospectus of the smart healthcare industry
Interested readers of this book will include anyone working in or involved in smart healthcare research which includes, but is not limited to healthcare specialists, computer science engineers, electronics engineers, systems engineers, and pharmaceutical practitioners.
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