This book presents the major advances in techniques to preserve privacy and security requirements connected with the use of AI and machine learning (ML) to analyse and manage sensitive data in MCPSs. The advances in Internet of things and artificial intelligence (AI) have witnessed great progress on healthcare technologies in several application domains. In particular, the interconnection between the physical spaces, characterized by physical devices able to collect users’ health information, with the cyberspace, also known as the virtual space, has fostered the development of intelligent Medical Cyber-Physical Systems (MCPSs) with the capability to deliver real-time healthcare services. On the other hand, the potential innovation that these technologies bring to improve patient care, by remotely analysing health parameters using medical devices, advanced smart sensors, and AI, is hampered by security and privacy challenges related to the managed sensitive data. 

Starting from the state of the art on AI and ML for medical applications and digital health, an accurate analysis of privacy and security risks associated with the use of the MCPSs is presented. Then, Digital Twins are introduced as a significant technique to enhance decision-making through learning and reasoning of collected on-field real-time data. Moreover, decentralized healthcare data management approaches based on federated learning, tiny machine learning, and blockchain technologies have been introduced to shift control and responsibility of healthcare data management from individual centralized entities to a more distributed structure, preserving privacy and security. Finally, the application of AI-based security monitoring approaches in healthcare is discussed. 

In this book, both theoretical and practical approaches are used to allow readers to understand complex topics and concepts easily also through real-life scenarios.

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This book presents the major advances in techniques to preserve privacy and security requirements connected with the use of AI and machine learning (ML) to analyse and manage sensitive data in MCPSs.

Les mer

Chapter 1: Artificial Intelligence and Machine Learning for Medical Applications and Digital Health.- Chapter 2: Healthcare & Cyberspace: Medical Cyber-Physical Systems.- Chapter 3: Privacy Risk Analysis in Medical Cyber–Physical Systems.- Chapter 4: Healthcare & Cyberspace: From Cyber-Physical Systems to Medical Digital Twins.- Chapter 5: Federated Learning in Agents Based Cyber-Physical Systems.- Chapter 6: Application of Solid Platform and Federated Learning for Decentralized Health Data Management.- Chapter 7: TinyML and Federated Learning in Resource-Constrained Medical Devices.- Chapter 8: The Role of Blockchain in AI-Driven Medical Cyber-Physical Systems.- Chapter 9: Artificial Intelligence Applications in Healthcare Security.

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This book presents the major advances in techniques to preserve privacy and security requirements connected with the use of AI and machine learning (ML) to analyse and manage sensitive data in MCPSs. The advances in Internet of things and artificial intelligence (AI) have witnessed great progress on healthcare technologies in several application domains. In particular, the interconnection between the physical spaces, characterized by physical devices able to collect users’ health information, with the cyberspace, also known as the virtual space, has fostered the development of intelligent Medical Cyber-Physical Systems (MCPSs) with the capability to deliver real-time healthcare services. On the other hand, the potential innovation that these technologies bring to improve patient care, by remotely analysing health parameters using medical devices, advanced smart sensors, and AI, is hampered by security and privacy challenges related to the managed sensitive data. 

Starting from the state of the art on AI and ML for medical applications and digital health, an accurate analysis of privacy and security risks associated with the use of the MCPSs is presented. Then, Digital Twins are introduced as a significant technique to enhance decision-making through learning and reasoning of collected on-field real-time data. Moreover, decentralized healthcare data management approaches based on federated learning, tiny machine learning, and blockchain technologies have been introduced to shift control and responsibility of healthcare data management from individual centralized entities to a more distributed structure, preserving privacy and security. Finally, the application of AI-based security monitoring approaches in healthcare is discussed. 

In this book, both theoretical and practical approaches are used to allow readers to understand complex topics and concepts easily also through real-life scenarios.

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Depicts use of advanced Machine Learning and Artificial Intelligence techniques for analysis of medical data and systems Written by experts in the field Focuses on methods, techniques, technologies, and tools related to medical cyber-physical systems
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GPSR Compliance The European Union's (EU) General Product Safety Regulation (GPSR) is a set of rules that requires consumer products to be safe and our obligations to ensure this. If you have any concerns about our products you can contact us on ProductSafety@springernature.com. In case Publisher is established outside the EU, the EU authorized representative is: Springer Nature Customer Service Center GmbH Europaplatz 3 69115 Heidelberg, Germany ProductSafety@springernature.com
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Produktdetaljer

ISBN
9783031707742
Publisert
2025-01-23
Utgiver
Vendor
Springer International Publishing AG
Høyde
235 mm
Bredde
155 mm
Aldersnivå
Research, P, 06
Språk
Product language
Engelsk
Format
Product format
Innbundet

Om bidragsyterne

Massimo Ficco is Full Professor at the Computer Science Department of the University of Salerno, Italy. His major research interests include security and reliability aspects of critical infrastructures. Currently, his scientific research and dissemination activities concern the use of machine learning and artificial intelligence in the context of malware analysis and IoT systems. Currently, he heads the laboratory of Internet of Things (IoTResearch).

Gianni D'Angelo is an Associate Professor at the University of Salerno, Italy. His research interests concern with the development and implementation of algorithms based on Artificial Intelligence, Deep Learning, Machine Learning, Explainable Artificial Intelligence, and parallel programming applied in various scientific and industrial fields.