The fast-growing number of patients suffering from various ailments has overstretched the carrying capacity of traditional healthcare systems. This handbook addresses the increased need to tackle security issues and preserve patients’ privacy concerns in Artificial Intelligence of Medical Things (AIoMT) devices and systems. Handbook of Security and Privacy of AI-Enabled Healthcare Systems and the Internet of Medical Things provides new insights into the deployment, application, management, and benefits of AIoMT by examining real-world scenarios. The handbook takes a critical look at existing security designs and offers solutions to revamp traditional security architecture, including the new design of effi cient intrusion detection algorithms, attack prevention techniques, and both cryptographic and noncryptographic solutions. The handbook goes on to discuss the critical security and privacy issues that affect all parties in the healthcare ecosystem and provides practical AI-based solutions. This handbook offers new and valuable information that will be highly beneficial to educators, researchers, and others.
The fast-growing number of patients suffering from various ailments has overstretched the carrying capacity of traditional healthcare systems. This handbook addresses the increased need to tackle security issues and preserve patients’ privacy concerns in Artificial Intelligence of Medical Things (AIoMT) devices and systems. Handbook of Security and Privacy of AI-Enabled Healthcare Systems and the Internet of Medical Things provides new insights into the deployment, application, management, and benefits of AIoMT by examining real-world scenarios. The handbook takes a critical look at existing security designs and offers solutions to revamp traditional security architecture, including the new design of effi cient intrusion detection algorithms, attack prevention techniques, and both cryptographic and noncryptographic solutions. The handbook goes on to discuss the critical security and privacy issues that affect all parties in the healthcare ecosystem and provides practical AI-based solutions. This handbook offers new and valuable information that will be highly beneficial to educators, researchers, and others. .
As the demand for advanced technologies to revolutionize patient care intensifies, the medical industry faces a pressing need to confront challenges hindering the assimilation of AI-enhanced healthcare systems. Issues such as data interoperability, ethical considerations, and the translation of AI advancements into practical clinical applications pose formidable hurdles that demand immediate attention. It is within this context of challenges and opportunities that the book, Clinical Practice and Unmet Challenges in AI-Enhanced Healthcare Systems promises to pave the way for a transformative era in healthcare. The book serves as a comprehensive guide for academic scholars, researchers, and healthcare professionals navigating the dynamic landscape of data-driven, AI-enhanced healthcare. By showcasing the latest advancements, the book empowers its readers to not only comprehend the existing frontiers in data sciences and healthcare technologies but also to actively contribute to overcoming obstacles. Through detailed case studies and practical guidance, the publication equips its audience with the skills necessary to implement AI in various clinical settings.
Smart cities are experiencing a rapid evolution. The integration of technologies such as 5G, Internet of Things (IoT), Artificial Intelligence (AI), and blockchain has ushered in transformative applications, enhancing the quality of urban life. However, this evolution comes with its own challenges, most notably in security and privacy. Secure and Intelligent IoT-Enabled Smart Cities addresses these concerns, exploring theoretical frameworks and empirical research findings. The book embarks on the foundational elements of the Internet of Things, delving into the convergence of IoT and smart city applications, elucidating the layered architecture of IoT, and highlighting the security issues inherent in IoT-enabled Smart Cities. This book pinpoints the challenges smart city infrastructures face and offers innovative and pragmatic solutions to fortify their security. This book targets professionals and researchers immersed in the dynamic field of secure and intelligent environments within IoT-enabled smart city applications. It is a valuable resource for executives grappling with the strategic implications of emerging technologies in smart healthcare, smart parking, smart manufacturing, smart transportation, and beyond.
Recently, the fields of Artificial Intelligence (AI) and the Internet of Things (IoT) have revolutionized numerous industries, including healthcare. The convergence of AI and IoT has given birth to smart healthcare systems, transforming the way we deliver, receive, and experience healthcare services. This book explores the profound impact of these technologies on healthcare and presents a comprehensive overview of their applications, challenges, and prospects. Smart Healthcare Systems: AI and IoT Perspectives addresses various aspects of how smart healthcare can be used to detect and analyze diseases, the underlying methodologies, and related security concerns. It also discusses healthcare as a multidisciplinary field that involves a range of sectors such as the financial system, social factors, health technologies, and organizational structures that affect individuals, families, institutions, organizations, and populations’ healthcare. The book presents the goals of healthcare services which include patient safety, timeliness, effectiveness, efficiency, and equity. An outline of what smart healthcare consists of which is m-health, e-health, electronic resource management, smart and intelligent home services, and medical devices is included. Along with highlights on how AI and IoT-enabled healthcare technologies are suitable for remote health monitoring, including rehabilitation, and assisted ambient living. Rounding the offers of this book out is that it also covers how healthcare analytics can be applied to the data gathered from different areas to improve healthcare at a minimum expense. Researchers, Academicians, Industry, R&D Organizations, medical professionals, PG students, and policymakers in the fields of artificial intelligence, the internet of things, healthcare informatics, biomedical engineering, medical informatics, and related subjects can use this book to assist them in making appropriate decisions regarding these emerging disciplines.
Federated Learning for Digital Healthcare Systems critically examines the key factors that contribute to the problem of applying machine learning in healthcare systems and investigates how federated learning can be employed to address the problem. The book discusses, examines, and compares the applications of federated learning solutions in emerging digital healthcare systems, providing a critical look in terms of the required resources, computational complexity, and system performance. In the first section, chapters examine how to address critical security and privacy concerns and how to revamp existing machine learning models. In subsequent chapters, the book's authors review recent advances to tackle emerging efficient and lightweight algorithms and protocols to reduce computational overheads and communication costs in wireless healthcare systems. Consideration is also given to government and economic regulations as well as legal considerations when federated learning is applied to digital healthcare systems. - Provides insights into real-world scenarios of the design, development, deployment, application, management, and benefits of federated learning in emerging digital healthcare systems - Highlights the need to design efficient federated learning-based algorithms to tackle the proliferating security and patient privacy issues in digital healthcare systems - Reviews the latest research, along with practical solutions and applications developed by global experts from academia and industry
The book is a comprehensive guide that explores the use of artificial intelligence and machine learning in drug discovery and development covering a range of topics, including the use of molecular modeling, docking, identifying targets, selecting compounds, and optimizing drugs. The intersection of Artificial Intelligence (AI) and Machine Learning (ML) within the field of drug design and development represents a pivotal moment in the history of healthcare and pharmaceuticals. The remarkable synergy between cutting-edge technology and the life sciences has ushered in a new era of possibilities, offering unprecedented opportunities, formidable challenges, and a tantalizing glimpse into the future of medicine. AI can be applied to all the key areas of the pharmaceutical industry, such as drug discovery and development, drug repurposing, and improving productivity within a short period. Contemporary methods have shown promising results in facilitating the discovery of drugs to target different diseases. Moreover, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the vast amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs that can be repurposed for alternative use in medicine. This book is a comprehensive exploration of this dynamic and rapidly evolving field. In an era where precision and efficiency are paramount in drug discovery, AI and ML have emerged as transformative tools, reshaping the way we identify, design, and develop pharmaceuticals. This book is a testament to the profound impact these technologies have had and will continue to have on the pharmaceutical industry, healthcare, and ultimately, patient well-being. The editors of this volume have assembled a distinguished group of experts, researchers, and thought leaders from both the AI, ML, and pharmaceutical domains. Their collective knowledge and insights illuminate the multifaceted landscape of AI and ML in drug design and development, offering a roadmap for navigating its complexities and harnessing its potential. In each section, readers will find a rich tapestry of knowledge, case studies, and expert opinions, providing a 360-degree view of AI and ML’s role in drug design and development. Whether you are a researcher, scientist, industry professional, policymaker, or simply curious about the future of medicine, this book offers 19 state-of-the-art chapters providing valuable insights and a compass to navigate the exciting journey ahead. Audience The book is a valuable resource for a wide range of professionals in the pharmaceutical and allied industries including researchers, scientists, engineers, and laboratory workers in the field of drug discovery and development, who want to learn about the latest techniques in machine learning and AI, as well as information technology professionals who are interested in the application of machine learning and artificial intelligence in drug development.
In the field of healthcare technology, the Internet of Medical Things (IoMT) stands at the forefront of progress, revolutionizing patient care through advanced monitoring and treatment modalities. However, this digital transformation brings forth a new challenge the vulnerability of sensitive medical data to cyber threats. Lightweight Digital Trust Architectures in the Internet of Medical Things (IoMT) examines ways to fortify IoMT against potential breaches through the exploration of these trust architectures. Delving deep into data privacy technologies, the book examines the implications of regulatory frameworks such as GDPR, HIPAA, and cybersecurity law. It assesses traditional security methods and considers innovative approaches, offering insights into certificate generation, digital identification, and the optimization of network protocols for secure data transmission.Lightweight Digital Trust Architectures in the Internet of Medical Things (IoMT) illuminates the path forward for IoMT security. Its objectives are multi-faceted: from unraveling the intricacies of the health chain to dissecting the role of lightweight cryptographic key agreement mechanisms in safeguarding medical data. The book grapples with the challenges and advantages of implementing compact cryptographic techniques in healthcare, particularly within the decentralized framework of IoMT. By exploring the potential of Federated Learning (FL) in bolstering privacy and improving healthcare outcomes, the book aims to equip researchers, healthcare professionals, and IT experts with valuable knowledge. Through real-world case studies, it endeavors to pave the way for a future where security and efficiency seamlessly integrate in IoMT.
"Digital Transformation in Healthcare 5.0: IoT, AI, and Digital Twin" provides a comprehensive overview of the integration of cutting-edge technology with healthcare, from the Fourth Industrial Revolution (4IR) to the introduction of IoT, AI, and Digital Twin technologies. This in-depth discussion of the digital revolution expanding the healthcare industry covers a wide range of topics, including digital disruption in healthcare delivery, the impact of 4IR and Health 4.0, e-health services and applications, virtual reality's impact on accessible healthcare delivery, digital twins and dietary health technologies, big data analytics in healthcare systems, machine learning models for cost-effective healthcare delivery systems, affordable healthcare with machine learning, enhanced biomedical signal processing with machine learning, and data-driven AI for information retrieval of biomedical images.
IoMT Applications in Healthcare 5.0 introduces the concept of Healthcare 5.0, emphasizing the transformative potential of intelligent diagnosis, remote treatment, and smart health management. Sections define IoMT and its multi-tiered architecture, discussing its pivotal role in improving electronic device productivity in healthcare, explore IoMT applications in Healthcare 5.0, discuss remote physical therapy using IoMT in Healthcare 5.0, delve into the security and privacy considerations of IoMT in healthcare, and address how to overcome challenges and future directions in IoMT. The book concludes by summarizing key findings, providing a comprehensive overview of the discussed topics.Written by experts in the field, this is a key text for Biomedical Engineers, Data Scientists, and IoT Developers and specialists who work in the Internet of Medical Things. Graduate students of IoT engineering, biology, biostatistics, biomedical sciences, bioinformatics, health-informatics, and Interdisciplinary sciences will also find this an invaluable resource. - Provides an integrative and multidisciplinary approach to Healthcare 5.0 - Covers recent advancement in IoMT in transitioning healthcare - Discusses the evolution of healthcare and IoMT
