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'In mental health care, artificial intelligence (AI) tools can enhance diagnostic accuracy, personalize treatment plans, and provide support through virtual therapy and chatbots that offer real-time assistance. These technologies can help identify early signs of mental health issues by analyzing patterns in speech, behavior, and physiological data. However, the integration of AI also raises concerns about privacy, data security, and the potential for algorithmic bias, which could impact quality of care. As AI continues to evolve, its role in psychological well-being and healthcare will depend on addressing these ethical and practical considerations while harnessing its potential to improve mental health outcomes and streamline healthcare delivery. AI Technologies and Advancements for Psychological Well-Being and Healthcare discusses the latest innovations in AI that are transforming the landscape of mental health and healthcare services. This book explores how AI applications, such as machine learning algorithms and natural language processing, are enhancing diagnostic accuracy, personalizing treatment options, and improving patient outcomes. Covering topics such as behavioral artificial intelligence, medical diagnosis, and precision medicine, this book is an excellent resource for mental health professionals, healthcare providers and administrators, AI and data scientists, academicians, researchers, healthcare policymakers, and more.'--

Digitalization and the Transformation of the Healthcare Sector provides a compilation of critical aspects that collectively serve to underscore the potential digital technologies advances can bring to healthcare delivery. It discusses the potential of e-health and lays out a clear road map and vision for achieving high quality, high value care for all. Covering topics such as 5G technology, metaverse, and telemedicine, this book is an excellent resource for researchers, academicians, clinicians, allied health workers, managers in healthcare, policymakers, graduate and postgraduate students, and more.

The healthcare sector is at a critical juncture, facing the pressing need to integrate generative AI technologies responsibly. Despite the promising benefits, such as improved diagnostics, personalized treatments, and streamlined operations, the adoption of AI in healthcare poses significant challenges. These challenges include ethical dilemmas, regulatory complexities, and the need for governance frameworks to ensure the technology's responsible use. Revolutionizing the Healthcare Sector with AI offers a comprehensive solution to these challenges. It provides a deep dive into the adoption, integration, scalability, and sustainability of generative AI in healthcare and a thorough analysis of governance, ethical, and regulatory issues. By offering insights from researchers, practitioners, patients, and policymakers, this book is a platform for responsible AI adoption in healthcare. This book is not just a theoretical exploration but a practical guide for all stakeholders in the healthcare industry. It offers frameworks for integrating generative AI responsibly, ensuring that ethical considerations and regulatory requirements are met. By providing real-world case studies, educational initiatives, and future trend analyses, this book equips readers with the knowledge and tools needed to navigate the complexities of AI integration in healthcare, ultimately leading to improved patient outcomes and a more efficient healthcare system.

The evolving healthcare landscape is now challenging healthcare providers and researchers to deliver personalized care considering individual genetic, lifestyle, and environmental factors. To tailor treatments and interventions to each patient's unique profile in a timely manner, they need to harness the vast amounts of readily available data. Artificial intelligence (AI) has immense potential to revolutionize precision health by enabling the analysis of large datasets and generating actionable insights for personalized care. However, implementing AI in healthcare comes with challenges, including ethical considerations, regulatory frameworks, and the need for healthcare professionals to develop competencies in AI technologies. Precision Health in the Digital Age: Harnessing AI for Personalized Care offers a comprehensive solution for healthcare professionals, researchers, and policymakers seeking to leverage AI in precision health. The book explores the latest advancements in AI technologies, showcases their applications in precision medicine, and provides a roadmap for integrating AI into healthcare systems. It offers practical insights for improving patient outcomes through case studies and real-world examples of successful implementations of AI-driven precision medicine and health solutions. Moreover, the book discusses AI's ethical, legal, and societal implications in personalized healthcare practices, guiding compliance with data protection and patient privacy laws. It highlights the role of AI in fostering communication and collaboration between healthcare providers and patients, emphasizing its potential to enhance patient engagement and adherence. Precision Health in the Digital Age: Harnessing AI for Personalized Care is an excellent resource for understanding the opportunities and challenges of leveraging AI for personalized care delivery. It offers insights into future trends and potential challenges in the field.

Healthcare and pharmaceuticals are rapidly advancing with technological innovations, and the lack of understanding of AI algorithms poses a significant challenge in these fields. The need for more transparency in AI decision-making processes raises concerns about accountability, ethical implications, and regulatory compliance. As stakeholders in these critical sectors seek clarity and understanding, Analyzing Explainable AI in Healthcare and the Pharmaceutical Industry provides a reliable resource to discover new solutions. This book serves as a comprehensive guide, unraveling the complexities of explainable artificial intelligence (XAI) and its pivotal role in transforming healthcare and pharmaceutical practices. Demystifying AI algorithms and revealing their decision-making mechanisms equips readers with the foundational knowledge needed to confidently navigate AI integration in these domains. From healthcare professionals to policymakers, its insights cater to a diverse audience, fostering cross-disciplinary collaboration and facilitating informed decision-making. Through real-world case studies, ethical explorations, and regulatory insights, this book addresses the challenges posed by unclear AI processes and provides actionable solutions. Promoting transparency, fairness, and accountability empowers stakeholders to harness the full potential of XAI while mitigating risks and ensuring compliance. This book emerges as a vital resource, guiding the journey towards responsible and impactful AI adoption in healthcare and pharmaceutical settings.

The healthcare industry is grappling with numerous challenges, including rising costs, inefficiencies in service delivery, and the need for personalized treatment approaches. Traditional healthcare management and delivery methods must be improved in addressing these issues, leading to a growing demand for innovative solutions. Additionally, the exponential growth of medical data and the complexity of biomedical research and biotechnology presents a daunting challenge in harnessing this data effectively for improved patient care and medical advancements. There is a pressing need for a comprehensive understanding of how artificial intelligence (AI) can be leveraged to tackle these challenges and drive meaningful change in the healthcare sector. Future of AI in Biomedicine and Biotechnology offers a timely and insightful solution to the challenges faced by the healthcare industry. By exploring the intersection of AI with biomedical sciences and biotechnology, this book provides a comprehensive guide to harnessing the power of AI for transformative healthcare innovation. Through a series of expertly curated chapters, the book delves into critical topics such as machine learning, medical imaging, natural language processing, drug discovery, personalized medicine, and telemedicine, among others, offering readers a deep understanding of AI's potential impact on healthcare. This book is not just a theoretical exploration; it is a practical roadmap for healthcare professionals, researchers, policymakers, and entrepreneurs seeking to navigate the complexities of AI in healthcare. By providing real-world examples, case studies, and future trends, the book equips readers with the knowledge and tools needed to harness the full potential of AI in biomedicine and biotechnology. Whether you want to optimize healthcare delivery, develop innovative healthcare technologies, or shape healthcare policy, Future of AI in Biomedicine and Biotechnology is invaluable to inspire and inform your journey towards a more efficient, effective, and personalized healthcare system.

Alzheimer's disease (AD) poses a significant global health challenge, with an estimated 50 million people affected worldwide and no known cure. Traditional methods of diagnosis and prediction often rely on subjective assessments. They are limited in detecting the disease early, leading to delayed intervention and poorer patient outcomes. Additionally, the complexity of AD, with its multifactorial etiology and diverse clinical manifestations, requires a multidisciplinary approach for effective management. AI-Driven Alzheimer's Disease Detection and Prediction offers a groundbreaking solution by leveraging advanced artificial intelligence (AI) techniques to enhance early diagnosis and prediction of AD. This edited book provides a comprehensive overview of state-of-the-art research, methodologies, and applications at the intersection of AI and AD detection. By bridging the gap between traditional diagnostic methods and cutting-edge technology, this book facilitates knowledge exchange, fosters interdisciplinary collaboration, and contributes to innovative solutions for AD management. It also benefits data scientists, engineers, policymakers, and professionals in the pharmaceutical and biotechnology industries. Graduate students interested in healthcare and technology will find accessible information on the latest developments in AI-driven approaches to AD detection and prediction.

While ultra-high field strength diagnosis technologies and artificial intelligence have propelled medicine imaging towards microstructure analysis and precise medicine, persistent challenges remain. These range from long scanning times to motion sensitivity and issues with imaging quality for certain types of tissue. Medical Robotics and AI-Assisted Diagnostics for a High-Tech Healthcare Industry summarizes emerging techniques, outlines clinical applications, and confronts the challenges head-on, proposing avenues for further research. It explores emerging techniques such as human-like robotics, medical Internet of Things (IoT), low-cost CT scanners, portable MRI devices, and breakthroughs in diagnosis technologies like zero echo time (ZTM) and compressed sensing volume interpolation breath-holding test sequences (CS-VIBE). This book provides an overview of the current state of medical imaging and clinical diagnosis applications, then expands into a roadmap for the future, envisioning the seamless integration of medical robotics and AI-assisted applications in the high-tech healthcare industry. As the influence of artificial intelligence continues to grow, the book serves as a clarion call for collaborative efforts, increased research, and unified strategies to navigate the challenges and harness the opportunities presented by the high-tech medical industry. This book is ideal for medical analysts, healthcare scientists, biotechnology analysts, scholars, researchers, academics, professionals, engineers, and students worldwide. It consolidates contributions from a broad spectrum of experts to share ideas, methodologies, technologies, and approaches that can collectively address the challenges associated with the convergence of machine learning, deep learning, computer vision, AI-integrated applications, IoT-based technologies, human-like robotics, healthcare data analytics, and the application of biotechnology in the era of an AI-centric industry.

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. This impressive research book calls a diverse audience, including researchers in IoMT and healthcare informatics, healthcare professionals, students, IT professionals, policymakers, software developers, data scientists, cybersecurity professionals, technology consultants, and academics. By catering to this broad spectrum, the book acts as a unifying resource, ensuring that stakeholders across the healthcare sector possess the requisite understanding to navigate the intricate intersection of technology, security, and patient well-being.

Healthcare professionals face a range of challenges in modern clinical medicine, from managing neurodegenerative disorders like Alzheimer's disease to treating allergic rhinitis in elderly populations. These challenges require innovative solutions, as traditional diagnostic and treatment methods may only sometimes be effective. How can clinicians navigate these challenges and provide the best possible care for their patients? Advancements in Clinical Medicine is a resource that provides practical solutions to these challenges through innovative approaches like machine learning integration and super-resolution reconstruction techniques revolutionize how we approach diagnosis and treatment. By leveraging cutting-edge technologies like artificial intelligence, this book equips scholars and practitioners with the tools they need to tackle even the most daunting medical challenges head-on. Seasoned healthcare professionals and an academic scholars seeking to stay at the forefront of medical research will find this book to be the ultimate guide to navigating the complexities of modern clinical medicine. Packed with actionable insights and groundbreaking discoveries, it promises to revolutionize how we understand and approach healthcare delivery.

The healthcare industry is predominantly moving towards affordable, accessible, and quality health care. All organizations are striving to build communication compatibility among the wide range of devices that have operated independently. Recent developments in electronic devices have boosted the research in the medical imaging field. It incorporates several medical imaging techniques and achieves an important goal for health improvement all over the world. Despite the significant advances in high-resolution medical instruments, physicians cannot always obtain the full amount of information directly from the equipment outputs, and a large amount of data cannot be easily exploited without a computer. Machine Learning and AI Techniques in Interactive Medical Image Analysis discusses how clinical efficiency can be improved by investigating the different types of intelligent techniques and systems to get more reliable and accurate diagnostic conclusions. This book further introduces segmentation techniques to locate suspicious areas in medical images and increase the segmentation accuracy. Covering topics such as computer-aided detection, intelligent techniques, and machine learning, this premier reference source is a dynamic resource for IT specialists, computer scientists, diagnosticians, imaging specialists, medical professionals, hospital administrators, medical students, medical technicians, librarians, researchers, and academicians.

Technological advancements have enhanced all functions of society and revolutionized the healthcare field. Smart healthcare applications and practices have grown within the past decade, strengthening overall care. Biomedical signals observe physiological activities, which provide essential information to healthcare professionals. Biomedical signal processing can be optimized through artificial intelligence (AI) and machine learning (ML), presenting the next step towards smart healthcare. AI-Enabled Smart Healthcare Using Biomedical Signals will not only cover the mathematical description of the AI- and ML-based methods, but also analyze and demonstrate the usability of different AI methods for a range of biomedical signals. The book covers all types of biomedical signals helpful for smart healthcare applications. Covering topics such as automated diagnosis, emotion identification, and frequency discrimination techniques, this premier reference source is an excellent resource for healthcare administration, biomedical engineers, medical laboratory technicians, medical technology assistants, computer scientists, libraries, students and faculty of higher education, researchers, and academicians.

Artificial intelligence (AI) technology has been very successful across fields such as healthcare, security, precision agriculture, smart city, and autonomous driving and promises numerous benefits for social development, economic growth, wellbeing management, and human healthcare. Various intelligent healthcare applications have been created in order to assist patient healthcare and must be studied further. AI Applications for Disease Diagnosis and Treatment provides the current advances and applications of artificial intelligence applications in healthcare such as disease diagnosis, diet proposal, drug prescription and tracking, and physical and psychological assistance. Covering topics such as assistive healthcare, robotics, and machine learning, it is ideal for healthcare professionals, researchers, data analysts, academicians, practitioners, scholars, instructors, and students.

As there are no proper medical tests available to predict certain diseases such as Alzheimer's and Parkinson's at an early stage, there is a need to further study and consider the potential uses of bio- and nature-inspired algorithms and future technologies such as machine learning in correlation to disease detection and treatment. Bio-Inspired Algorithms and Devices for Treatment of Cognitive Diseases Using Future Technologies considers new tools for early detection of cognitive brain diseases using devices and algorithms whose basic concept is taken from nature and discusses design, analysis, and application of various bionics or bio-inspired algorithms. Covering topics such as depression and cognitive science, this publication is an ideal resource for researchers, academicians, industry professionals, psychologists, psychiatrists, nurses, engineers, instructors, and students.

Data analytics is proving to be an ally for epidemiologists as they join forces with data scientists to address the scale of crises. Analytics examined from many sources can derive insights and be used to study and fight global outbreaks. Pandemic analytics is a modern way to combat a problem as old as humanity itself: the proliferation of disease. Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease explores different types of data and discusses how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more by applying cutting edge technology such as machine learning and data analytics in the wake of the COVID-19 pandemic. Covering a range of topics such as mental health analytics during COVID-19, data analysis and machine learning using Python, and statistical model development and deployment, it is ideal for researchers, academicians, data scientists, technologists, data analysts, diagnosticians, healthcare professionals, computer scientists, and students.

Autism spectrum disorder (ASD) is known as a neuro-disorder in which a person may face problems in interaction and communication with people, amongst other challenges. As per medical experts, ASD can be diagnosed at any stage or age but is often noticeable within the first two years of life. If caught early enough, therapies and services can be provided at this early stage instead of waiting until it is too late. ASD occurrences appear to have increased over the last couple of years leading to the need for more research in the field. It is crucial to provide researchers and clinicians with the most up-to-date information on the clinical features, etiopathogenesis, and therapeutic strategies for patients as well as to shed light on the other psychiatric conditions often associated with ASD. In addition, it is equally important to understand how to detect ASD in individuals for accurate diagnosing and early detection. Artificial Intelligence for Accurate Analysis and Detection of Autism Spectrum Disorder discusses the early detection and diagnosis of autism spectrum disorder enabled by artificial intelligence technologies, applications, and therapies. This book will focus on the early diagnosis of ASD through artificial intelligence, such as deep learning and machine learning algorithms, for confirming diagnosis or suggesting the need for further evaluation of individuals. The chapters will also discuss the use of artificial intelligence technologies, such as medical robots, for enhancing the communication skills and the social and emotional skills of children who have been diagnosed with ASD. This book is ideally intended for IT specialists, data scientists, academicians, scholars, researchers, policymakers, medical practitioners, and students interested in how artificial intelligence is impacting the diagnosis and treatment of autism spectrum disorder.

Health surveillance and intelligence play an important role in modern health systems as more data must be collected and analyzed. It is crucial that this data is interpreted and analyzed effectively and efficiently in order to assist with diagnoses and predictions. Diagnostic Applications of Health Intelligence and Surveillance Systems is an essential reference book that examines recent studies that are driving artificial intelligence in the health sector and helping practitioners to predict and diagnose diseases. Chapters within the book focus on health intelligence and how health surveillance data can be made useful and meaningful. Covering topics that include computational intelligence, data analytics, mobile health, and neural networks, this book is crucial for healthcare practitioners, IT specialists, academicians, researchers, and students.

There are more than 80 different sleep disorders including insomnia, sleep apnea, restless leg syndrome, hypersomnia, circadian rhythm disorders, and parasomnia. Good sleep is necessary for optimal health and can affect hormone levels and weight. The use of artificial intelligence (AI) and biomedical signals and images can help in healthcare diagnostics that are related to these and other sleep disorders. Advancing the Investigation and Treatment of Sleep Disorders Using AI presents an overview of sleep disorders based on machine intelligence methods in order to learn and explore the latest advancements, developments, methods, systems, futuristic approaches, and algorithms towards sleep disorders and to address their challenges. This book also discusses recent and future advancements in various feature extraction techniques and machine learning methods. Covering topics such as biomedical signal processing, augmented reality for clinical investigation, and sleep disorder detection, this book is essential for sleep medicine practitioners, clinical psychologists, psychiatrists, medical technologists, doctors, IT specialists, biomedical engineers, researchers, graduate students, and academicians.

The field of healthcare is seeing a rapid expansion of technological advancement within current medical practices. The implementation of technologies including neural networks, multi-model imaging, genetic algorithms, and soft computing are assisting in predicting and identifying diseases, diagnosing cancer, and the examination of cells. Implementing these biomedical technologies remains a challenge for hospitals worldwide, creating a need for research on the specific applications of these computational techniques. Deep Neural Networks for Multimodal Imaging and Biomedical Applications provides research exploring the theoretical and practical aspects of emerging data computing methods and imaging techniques within healthcare and biomedicine. The publication provides a complete set of information in a single module starting from developing deep neural networks to predicting disease by employing multi-modal imaging. Featuring coverage on a broad range of topics such as prediction models, edge computing, and quantitative measurements, this book is ideally designed for researchers, academicians, physicians, IT consultants, medical software developers, practitioners, policymakers, scholars, and students seeking current research on biomedical advancements and developing computational methods in healthcare.

With the growing use of new technologies and artificial intelligence (AI) applications, intelligent systems can be used to manage large amounts of existing data in healthcare domains. Having more intelligent methods for accessing data allows medical professionals to more efficiently identify the best medical practices and more concrete solutions for diagnosing and treating a multitude of rare diseases. Intelligent Systems for Healthcare Management and Delivery provides relevant and advanced methodological, technological, and scientific approaches related to the application of sophisticated exploitation of AI, as well as providing insight into the technologies and intelligent applications that have received growing attention in recent years such as medical imaging, EMR systems, and drug development assistance. This publication fosters a scientific debate for new healthcare intelligent systems and sophisticated approaches for enhanced healthcare services and is ideally designed for medical professionals, hospital staff, rehabilitation specialists, medical educators, and researchers.

Advanced conceptual modeling techniques serve as a powerful tool for those in the medical field by increasing the accuracy and efficiency of the diagnostic process. The application of artificial intelligence assists medical professionals to analyze and comprehend a broad range of medical data, thus eliminating the potential for human error. Medical Diagnosis Using Artificial Neural Networks introduces effective parameters for improving the performance and application of machine learning and pattern recognition techniques to facilitate medical processes. This book is an essential reference work for academicians, professionals, researchers, and students interested in the relationship between artificial intelligence and medical science through the use of informatics to improve the quality of medical care.