Analytics and AI for Healthcare – serie

In the era of 'Algorithmic Medicine', the integration of Artificial Intelligence (AI) in healthcare holds immense potential to address critical challenges faced by the industry. Drawing upon the expertise and experience of the authors in medicine, data science, medical informatics, administration, and entrepreneurship, this textbook goes beyond theoretical discussions to outline practical steps for transitioning AI from the experimental phase to real-time clinical integration. Using the Translational Science methodology, each chapter of the book concisely and clearly addresses the key issues associated with AI implementation in healthcare. Covering technical, clinical, ethical, regulatory, and legal considerations, the authors present evidence-based solutions and frameworks to overcome these challenges. Engaging case studies and a literature review of peer-reviewed studies and official documents from reputed organizations provide a balanced perspective, bridging the gap between AI research and actual clinical practice.

In the era of 'Algorithmic Medicine', the integration of Artificial Intelligence (AI) in healthcare holds immense potential to address critical challenges faced by the industry. Drawing upon the expertise and experience of the authors in medicine, data science, medical informatics, administration, and entrepreneurship, this textbook goes beyond theoretical discussions to outline practical steps for transitioning AI from the experimental phase to real-time clinical integration. Using the Translational Science methodology, each chapter of the book concisely and clearly addresses the key issues associated with AI implementation in healthcare. Covering technical, clinical, ethical, regulatory, and legal considerations, the authors present evidence-based solutions and frameworks to overcome these challenges. Engaging case studies and a literature review of peer-reviewed studies and official documents from reputed organizations provide a balanced perspective, bridging the gap between AI research and actual clinical practice.

Data-driven science has become a major decision-making aid for the diagnosis and treatment of disease. Computational and visual analytics enables effective exploration and sense making of large and complex data through the deployment of appropriate data science methods, meaningful visualisation and human-information interaction.This edited volume covers state-of-the-art theory, method, models, design, evaluation and applications in computational and visual analytics in desktop, mobile and immersive environments for analysing biomedical and health data. The book is focused on data-driven integral analysis, including computational methods and visual analytics practices and solutions for discovering actionable knowledge in support of clinical actions in real environments.By studying how data and visual analytics have been implemented into the healthcare domain, the book demonstrates how analytics influences the domain through improving decision making, specifying diagnostics, selecting the best treatments and generating clinical certainty.

Data-driven science has become a major decision-making aid for the diagnosis and treatment of disease. Computational and visual analytics enables effective exploration and sense making of large and complex data through the deployment of appropriate data science methods, meaningful visualisation and human-information interaction.This edited volume covers state-of-the-art theory, method, models, design, evaluation and applications in computational and visual analytics in desktop, mobile and immersive environments for analysing biomedical and health data. The book is focused on data-driven integral analysis, including computational methods and visual analytics practices and solutions for discovering actionable knowledge in support of clinical actions in real environments.By studying how data and visual analytics have been implemented into the healthcare domain, the book demonstrates how analytics influences the domain through improving decision making, specifying diagnostics, selecting the best treatments and generating clinical certainty.

Data analysis plays a vital role in guiding medical treatment plans, patient care, and the formulation of control and prevention policies in the field of healthcare. In today's era, researchers in these domains require a firm grasp of data, statistical concepts, and programming skills due to the increasing complexity of data. Reproducible analyses and cutting-edge statistical methods are becoming increasingly necessary.This book, which is both comprehensive and highly practical, addresses these challenges by laying a solid foundation of data and statistical theory for readers. Subsequently, it equips them with practical skills to conduct analyses using the powerful R programming language, widely used by statisticians. The book takes a gentle approach to help readers navigate data and statistical analysis using R, minimizing the learning curve. RStudio is used as the integrated development environment (IDE) for enhanced productivity for readers to run their R codes.Following a logical sequence commonly applied in medical and health research, the book covers fundamental concepts of data analysis and statistical modeling techniques. It provides readers, including those with limited statistical knowledge and programming skills, with hands-on experience through R programming.The online version of this book is available on bookdown.org, a publishing platform provided by RStudio, PBC specifically designed to host books written using the "bookdown" package in R. Additionally, all R codes and datasets in this book can be found on the author's GitHub repository.

Data analysis plays a vital role in guiding medical treatment plans, patient care, and the formulation of control and prevention policies in the field of healthcare. In today's era, researchers in these domains require a firm grasp of data, statistical concepts, and programming skills due to the increasing complexity of data. Reproducible analyses and cutting-edge statistical methods are becoming increasingly necessary.This book, which is both comprehensive and highly practical, addresses these challenges by laying a solid foundation of data and statistical theory for readers. Subsequently, it equips them with practical skills to conduct analyses using the powerful R programming language, widely used by statisticians. The book takes a gentle approach to help readers navigate data and statistical analysis using R, minimizing the learning curve. RStudio is used as the integrated development environment (IDE) for enhanced productivity for readers to run their R codes.Following a logical sequence commonly applied in medical and health research, the book covers fundamental concepts of data analysis and statistical modeling techniques. It provides readers, including those with limited statistical knowledge and programming skills, with hands-on experience through R programming.The online version of this book is available on bookdown.org, a publishing platform provided by RStudio, PBC specifically designed to host books written using the "bookdown" package in R. Additionally, all R codes and datasets in this book can be found on the author's GitHub repository.

Healthcare systems globally are grappling with how best to implement effective and efficient patient-centred care while simultaneously trying to contain runaway costs and provide high quality. This book explores the essential enabling role of digital health, taking a socio-technical perspective and looking at the key facets of technology, people and process in turn.This book examines the opportunities of key digital health components, demystifying digital health and demonstrating how to use its key precepts effectively. The book presents evidence and anecdotes from stakeholders around the world, demonstrating the global relevance and the ability of digital health to uplift and upskill care delivery as it is applied commercially. Bridging academic theory and practice, this is a functional and accessible text for all digital health stakeholders.The text introduces critical issues and is suitable reading for students, practitioners and researchers in digital health and all healthcare-related domains.

Healthcare systems globally are grappling with how best to implement effective and efficient patient-centred care while simultaneously trying to contain runaway costs and provide high quality. This book explores the essential enabling role of digital health, taking a socio-technical perspective and looking at the key facets of technology, people and process in turn.This book examines the opportunities of key digital health components, demystifying digital health and demonstrating how to use its key precepts effectively. The book presents evidence and anecdotes from stakeholders around the world, demonstrating the global relevance and the ability of digital health to uplift and upskill care delivery as it is applied commercially. Bridging academic theory and practice, this is a functional and accessible text for all digital health stakeholders.The text introduces critical issues and is suitable reading for students, practitioners and researchers in digital health and all healthcare-related domains.

This book combines technology and the medical domain. It covers advances in computer vision (CV) and machine learning (ML) that facilitate automation in diagnostics and therapeutic and preventive health care. The special focus on eXplainable Artificial Intelligence (XAI) uncovers the black box of ML and bridges the semantic gap between the technologists and the medical fraternity. Explainable AI in Healthcare: Unboxing Machine Learning for Biomedicine intends to be a premier reference for practitioners, researchers, and students at basic, intermediary levels and expert levels in computer science, electronics and communications, information technology, instrumentation and control, and electrical engineering.This book will benefit readers in the following ways: Explores state of art in computer vision and deep learning in tandem to develop autonomous or semi-autonomous algorithms for diagnosis in health care Investigates bridges between computer scientists and physicians being built with XAI Focuses on how data analysis provides the rationale to deal with the challenges of healthcare and making decision-making more transparent Initiates discussions on human-AI relationships in health care Unites learning for privacy preservation in health care

This book combines technology and the medical domain. It covers advances in computer vision (CV) and machine learning (ML) that facilitate automation in diagnostics and therapeutic and preventive health care. The special focus on eXplainable Artificial Intelligence (XAI) uncovers the black box of ML and bridges the semantic gap between the technologists and the medical fraternity. Explainable AI in Healthcare: Unboxing Machine Learning for Biomedicine intends to be a premier reference for practitioners, researchers, and students at basic, intermediary levels and expert levels in computer science, electronics and communications, information technology, instrumentation and control, and electrical engineering.This book will benefit readers in the following ways: Explores state of art in computer vision and deep learning in tandem to develop autonomous or semi-autonomous algorithms for diagnosis in health care Investigates bridges between computer scientists and physicians being built with XAI Focuses on how data analysis provides the rationale to deal with the challenges of healthcare and making decision-making more transparent Initiates discussions on human-AI relationships in health care Unites learning for privacy preservation in health care

This title demystifies artificial intelligence (AI) and analytics, upskilling individuals (healthcare professionals, hospital managers, consultants, researchers, students, and the population at large) around analytics and AI as it applies to healthcare.This book shows how the tools, techniques, technologies, and tactics around analytics and AI can be best leveraged and utilised to realise a healthcare value proposition of better quality, better access and high value for everyone every day, everywhere. The book presents a triumvirate approach including technical, business and medical aspects of data and analytics and by so doing takes a responsible approach to this key area.This work serves to introduce the critical issues in AI and analytics for healthcare to students, practitioners, and researchers.

This title demystifies artificial intelligence (AI) and analytics, upskilling individuals (healthcare professionals, hospital managers, consultants, researchers, students, and the population at large) around analytics and AI as it applies to healthcare.This book shows how the tools, techniques, technologies, and tactics around analytics and AI can be best leveraged and utilised to realise a healthcare value proposition of better quality, better access and high value for everyone every day, everywhere. The book presents a triumvirate approach including technical, business and medical aspects of data and analytics and by so doing takes a responsible approach to this key area.This work serves to introduce the critical issues in AI and analytics for healthcare to students, practitioners, and researchers.

This edited volume examines the transformative impact of AI technologies on global healthcare systems, with a focus on enhancing efficiency and accessibility. The content provides a comprehensive exploration of the principles and practices required to scale AI applications in healthcare, addressing areas such as diagnosis, treatment, and patient care.Key topics include data scalability, model deployment, and infrastructure design, highlighting the use of microservices, containerization, cloud computing, and big data technologies in building scalable AI systems. Discussions cover advancements in machine learning models, distributed processing, and transfer learning, alongside critical considerations such as continuous integration, data privacy, and ethics. Real-world case studies depict both the successes and challenges of implementing scalable AI across various healthcare environments, offering valuable insights for future advancements.This volume serves as a practical and theoretical guide for healthcare professionals, AI researchers, and technology enthusiasts seeking to develop or expand on AI-driven healthcare solutions to address global health challenges effectively.

This edited volume examines the transformative impact of AI technologies on global healthcare systems, with a focus on enhancing efficiency and accessibility. The content provides a comprehensive exploration of the principles and practices required to scale AI applications in healthcare, addressing areas such as diagnosis, treatment, and patient care.Key topics include data scalability, model deployment, and infrastructure design, highlighting the use of microservices, containerization, cloud computing, and big data technologies in building scalable AI systems. Discussions cover advancements in machine learning models, distributed processing, and transfer learning, alongside critical considerations such as continuous integration, data privacy, and ethics. Real-world case studies depict both the successes and challenges of implementing scalable AI across various healthcare environments, offering valuable insights for future advancements.This volume serves as a practical and theoretical guide for healthcare professionals, AI researchers, and technology enthusiasts seeking to develop or expand on AI-driven healthcare solutions to address global health challenges effectively.

This book comprehensively explores the latest technological advancements in healthcare, with a particular focus on the application of cutting-edge technologies, such as artificial intelligence (AI), computer vision, and robotics. The focus extends across crucial domains, such as disease diagnosis and monitoring, medical imaging, and the facilitation of remote healthcare services.The book provides a comprehensive overview of AI techniques for intelligent diagnoses, discussing how machine learning and deep learning models enhance accuracy and speed in medical imaging, diagnostics, and patient care. It also delves into the integration of AI with other disciplines, such as data science, computer vision, edge computing, robotics, and web development, to tackle complex medical challenges. Moreover, it highlights current trends and future prospects in surgery, rehabilitation, neuroscience, and automated healthcare systems, offering valuable insights into the future of technology-driven healthcare solutions. The chapters are authored by researchers and professionals from every region of the globe, including Africa, Asia, the Americas, Europe, and Oceania. This global contribution highlights the versatility and broad perspectives of the shared insights and conclusions presented in the book.This book is an essential guide for healthcare professionals, researchers, and enthusiasts eager to understand and actively contribute to shaping the future of healthcare through the integration of AI and other disciplines.

This book comprehensively explores the latest technological advancements in healthcare, with a particular focus on the application of cutting-edge technologies, such as artificial intelligence (AI), computer vision, and robotics. The focus extends across crucial domains, such as disease diagnosis and monitoring, medical imaging, and the facilitation of remote healthcare services.The book provides a comprehensive overview of AI techniques for intelligent diagnoses, discussing how machine learning and deep learning models enhance accuracy and speed in medical imaging, diagnostics, and patient care. It also delves into the integration of AI with other disciplines, such as data science, computer vision, edge computing, robotics, and web development, to tackle complex medical challenges. Moreover, it highlights current trends and future prospects in surgery, rehabilitation, neuroscience, and automated healthcare systems, offering valuable insights into the future of technology-driven healthcare solutions. The chapters are authored by researchers and professionals from every region of the globe, including Africa, Asia, the Americas, Europe, and Oceania. This global contribution highlights the versatility and broad perspectives of the shared insights and conclusions presented in the book.This book is an essential guide for healthcare professionals, researchers, and enthusiasts eager to understand and actively contribute to shaping the future of healthcare through the integration of AI and other disciplines.

This book looks at the integration of blockchain technology in healthcare settings, focusing on its potential to address security and privacy concerns of medical applications. From fragmented electronic health records (EHRs) to data breaches and interoperability issues, innovative solutions are necessary to unlock the full potential of health information and prevent the recurrence of such issues.Blockchain offers a promising framework for addressing these challenges. Its decentralized, tamper-resistant nature holds the key to building trust and transparency in healthcare data management. By leveraging blockchain technology, secure, interoperable systems empower patients to take control of their health information while facilitating a seamless collaboration among healthcare providers. Throughout this book, the authors explore the fundamental principles of blockchain technology and its applications within the healthcare landscape. From EHRs and patient consent management to pharmaceutical supply chains and clinical research, this book examines how blockchain can drive efficiency, enhance security, and ultimately improve patient outcomes.This book is intended for a broad audience, including healthcare professionals, patients, policymakers, and anyone interested in the intersection of technology and healthcare.

This book looks at the integration of blockchain technology in healthcare settings, focusing on its potential to address security and privacy concerns of medical applications. From fragmented electronic health records (EHRs) to data breaches and interoperability issues, innovative solutions are necessary to unlock the full potential of health information and prevent the recurrence of such issues.Blockchain offers a promising framework for addressing these challenges. Its decentralized, tamper-resistant nature holds the key to building trust and transparency in healthcare data management. By leveraging blockchain technology, secure, interoperable systems empower patients to take control of their health information while facilitating a seamless collaboration among healthcare providers. Throughout this book, the authors explore the fundamental principles of blockchain technology and its applications within the healthcare landscape. From EHRs and patient consent management to pharmaceutical supply chains and clinical research, this book examines how blockchain can drive efficiency, enhance security, and ultimately improve patient outcomes.This book is intended for a broad audience, including healthcare professionals, patients, policymakers, and anyone interested in the intersection of technology and healthcare.

This book focuses on using analytics and artificial intelligence (AI) to offer a high-quality value proposition to address dementia, a growing and concerning global healthcare problem. By enabling better decision-making support, minimizing medical errors and use of specific treatment strategies, digital technologies are key to effective care for People Living with Dementia (PLWD). By taking a unique triumvirate approach of focusing simultaneously on people issues, technical issues and process issues, the critical success issues are identified.The first point of concern is the different major stakeholders, including PLWD, their family carer, their challenges and major barriers and facilitators. Next, the vast pool of digital tools is considered, such as the different forms of mobile, platforms, sensors, IoT, chatbots, avatars, robots, augmented reality, virtual reality and mixed reality. This leads next to looking at the important considerations for the design and development of digital health solutions for priority areas of action in the Global Action Plan on Dementia, including co-design and research. By using a global perspective, the international examples help to better appreciate culture and practices, as countries differ significantly regarding many key issues pertaining to healthcare delivery. The editors endeavour to cover all areas pertinent to leveraging digital health solutions to facilitate the realization of value-driven healthcare delivery for all healthcare systems.This book is a new and comprehensive text, which would serve as an important resource for practitioners and professionals in healthcare and would be beneficial as a supplementary text for students undertaking studies relating to health informatics and public healthcare.

This book focuses on using analytics and artificial intelligence (AI) to offer a high-quality value proposition to address dementia, a growing and concerning global healthcare problem. By enabling better decision-making support, minimizing medical errors and use of specific treatment strategies, digital technologies are key to effective care for People Living with Dementia (PLWD). By taking a unique triumvirate approach of focusing simultaneously on people issues, technical issues and process issues, the critical success issues are identified.The first point of concern is the different major stakeholders, including PLWD, their family carer, their challenges and major barriers and facilitators. Next, the vast pool of digital tools is considered, such as the different forms of mobile, platforms, sensors, IoT, chatbots, avatars, robots, augmented reality, virtual reality and mixed reality. This leads next to looking at the important considerations for the design and development of digital health solutions for priority areas of action in the Global Action Plan on Dementia, including co-design and research. By using a global perspective, the international examples help to better appreciate culture and practices, as countries differ significantly regarding many key issues pertaining to healthcare delivery. The editors endeavour to cover all areas pertinent to leveraging digital health solutions to facilitate the realization of value-driven healthcare delivery for all healthcare systems.This book is a new and comprehensive text, which would serve as an important resource for practitioners and professionals in healthcare and would be beneficial as a supplementary text for students undertaking studies relating to health informatics and public healthcare.

This book centres on the topic of digital twins for superior healthcare decision support, as access is enabled to large volumes of multi-dimensional data such as patient’s electronic medical records, medical scans, and data. The reader learns about the possibility of a digital representation of analogous clinical cases built from data-driven models to represent and present relevant information and germane knowledge in context.Together with cutting-edge technologies, the authors share the ability of data-driven models to offer more efficient clinical decision support. The authors take a three-prong approach in the study of digital twins, the positive contributions made in other industries, the different types of applications and the numerous benefits offered. Artificial intelligence (AI) techniques, such as machine learning (ML) and deep learning (DL) algorithms, are discussed in the context of digital twins in healthcare applications. By looking at digital twins it is possible to reduce workflow challenges and provide fast and precise diagnosis. This then demonstrates how digital twins therefore support superior clinical decision-making. Importantly, the authors identify critical success issues, including co-design and research, for the design, development, and deployment of suitable digital twins.This book is written for the healthcare audience, professionals, physicians, medical administrators, managers, and IT practitioners. It also serves as a useful reference for senior-level undergraduate students and graduate students in health informatics and public health.

This book centres on the topic of digital twins for superior healthcare decision support, as access is enabled to large volumes of multi-dimensional data such as patient’s electronic medical records, medical scans, and data. The reader learns about the possibility of a digital representation of analogous clinical cases built from data-driven models to represent and present relevant information and germane knowledge in context.Together with cutting-edge technologies, the authors share the ability of data-driven models to offer more efficient clinical decision support. The authors take a three-prong approach in the study of digital twins, the positive contributions made in other industries, the different types of applications and the numerous benefits offered. Artificial intelligence (AI) techniques, such as machine learning (ML) and deep learning (DL) algorithms, are discussed in the context of digital twins in healthcare applications. By looking at digital twins it is possible to reduce workflow challenges and provide fast and precise diagnosis. This then demonstrates how digital twins therefore support superior clinical decision-making. Importantly, the authors identify critical success issues, including co-design and research, for the design, development, and deployment of suitable digital twins.This book is written for the healthcare audience, professionals, physicians, medical administrators, managers, and IT practitioners. It also serves as a useful reference for senior-level undergraduate students and graduate students in health informatics and public health.

The book provides an authoritative and accessible guide on integrating intelligent technologies, specifically Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), into modern eye care.Written by experts in ophthalmology, medical AI, and healthcare innovation, it offers a multidisciplinary exploration of how these tools are fundamentally redefining ophthalmic diagnostics, therapeutics, and surgical interventions. The core focus is on bridging the gap between technical AI innovations and their clinical deployment, offering a practical roadmap for clinicians and researchers. It combines cutting-edge research with real-world applications to demonstrate how AI is being used for the early and accurate detection of retinal diseases and glaucoma, enhancing surgical precision via robotics, and guiding personalized treatment plans. Three key areas are studied throughout the book. First, AI-powered imaging diagnostics, to explore how ML and DL models are used for the early and accurate detection of retinal diseases and glaucoma, supporting faster and more precise decision-making. Second, the application of AI-assisted surgical robotics in ophthalmology: In this topic, we shall examine how AI integration in robotic surgery enhances precision, reduces risk, and supports surgical planning. Third, ethical and regulatory considerations to address the responsible adoption of AI, including data governance, patient privacy, and health equity in ophthalmic care. These topics are essential for stakeholders navigating the clinical, technical, and ethical dimensions of AI implementation and adoption. Ultimately, the text aims to show how the responsible adoption of AI is actively improving clinical outcomes and reshaping the future of vision science.The primary audience for this book includes students taking advanced undergraduate or postgraduate courses on medical technology, medical imaging and healthcare bioinformatics. It would also serve as a useful guide for practicing ophthalmologists, optometrists, biomedical engineers, computer vision experts, and AI developers, working in the healthcare and medical related areas.

The book provides an authoritative and accessible guide on integrating intelligent technologies, specifically Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), into modern eye care.Written by experts in ophthalmology, medical AI, and healthcare innovation, it offers a multidisciplinary exploration of how these tools are fundamentally redefining ophthalmic diagnostics, therapeutics, and surgical interventions. The core focus is on bridging the gap between technical AI innovations and their clinical deployment, offering a practical roadmap for clinicians and researchers. It combines cutting-edge research with real-world applications to demonstrate how AI is being used for the early and accurate detection of retinal diseases and glaucoma, enhancing surgical precision via robotics, and guiding personalized treatment plans. Three key areas are studied throughout the book. First, AI-powered imaging diagnostics, to explore how ML and DL models are used for the early and accurate detection of retinal diseases and glaucoma, supporting faster and more precise decision-making. Second, the application of AI-assisted surgical robotics in ophthalmology: In this topic, we shall examine how AI integration in robotic surgery enhances precision, reduces risk, and supports surgical planning. Third, ethical and regulatory considerations to address the responsible adoption of AI, including data governance, patient privacy, and health equity in ophthalmic care. These topics are essential for stakeholders navigating the clinical, technical, and ethical dimensions of AI implementation and adoption. Ultimately, the text aims to show how the responsible adoption of AI is actively improving clinical outcomes and reshaping the future of vision science.The primary audience for this book includes students taking advanced undergraduate or postgraduate courses on medical technology, medical imaging and healthcare bioinformatics. It would also serve as a useful guide for practicing ophthalmologists, optometrists, biomedical engineers, computer vision experts, and AI developers, working in the healthcare and medical related areas.

This book directly addresses a critical challenge facing the healthcare system: the escalating prevalence and cost of chronic conditions. The solution lies in harnessing the simultaneous, rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML). The authors present a structured approach to designing, developing, and deploying intelligent digital health solutions to support the prevention and better self-management of chronic diseases, ultimately aiming to contain spiralling healthcare costs.This text provides a comprehensive guide to applying intelligent digital health to chronic care management. It first establishes the foundational essentials of digital health, detailing its socio-technical components, including crucial people, process, and technology aspects, and the indispensable role of AI/ML. Next, it deeply explores chronic conditions, emphasizing prevention and wellness management, while integrating key theories for supporting technology design and behavior change, such as co-creation, user-centered design (UCD), Task Technology Fit (TTF), nudge theory, and gamification. The book champions a personalized approach, integrating precision medicine and genomics into lifestyle interventions. A significant highlight is the presentation of several real-world case studies demonstrating practical applications, including solutions for pre-diabetes reversal, a diabetes monitoring device (DiaMonD), a post-discharge cardiac rehab app, and an asthma management tool utilizing AI for trigger prediction. By focusing on orchestration and automation, the text shows how to ensure seamless data integration across multiple devices (e.g., smartwatches and sensors), providing evidence-based support for clinicians and empowering patients to take charge of their health.This book is an essential text for graduate and senior level undergraduate students studying health informatics. For practitioners such as healthcare managers and IT consultants, entering the healthcare space as the text would inform them of both the efficacy and importance of as well as how to design appropriate effective and efficient electronic healthcare initiatives.

This book directly addresses a critical challenge facing the healthcare system: the escalating prevalence and cost of chronic conditions. The solution lies in harnessing the simultaneous, rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML). The authors present a structured approach to designing, developing, and deploying intelligent digital health solutions to support the prevention and better self-management of chronic diseases, ultimately aiming to contain spiralling healthcare costs.This text provides a comprehensive guide to applying intelligent digital health to chronic care management. It first establishes the foundational essentials of digital health, detailing its socio-technical components, including crucial people, process, and technology aspects, and the indispensable role of AI/ML. Next, it deeply explores chronic conditions, emphasizing prevention and wellness management, while integrating key theories for supporting technology design and behavior change, such as co-creation, user-centered design (UCD), Task Technology Fit (TTF), nudge theory, and gamification. The book champions a personalized approach, integrating precision medicine and genomics into lifestyle interventions. A significant highlight is the presentation of several real-world case studies demonstrating practical applications, including solutions for pre-diabetes reversal, a diabetes monitoring device (DiaMonD), a post-discharge cardiac rehab app, and an asthma management tool utilizing AI for trigger prediction. By focusing on orchestration and automation, the text shows how to ensure seamless data integration across multiple devices (e.g., smartwatches and sensors), providing evidence-based support for clinicians and empowering patients to take charge of their health.This book is an essential text for graduate and senior level undergraduate students studying health informatics. For practitioners such as healthcare managers and IT consultants, entering the healthcare space as the text would inform them of both the efficacy and importance of as well as how to design appropriate effective and efficient electronic healthcare initiatives.

As global healthcare faces rising costs and chronic disease, Physical AI emerges as a transformative solution. This book addresses a critical gap in literature, moving beyond high-level theory to provide a comprehensive roadmap for integration. By exploring the why, what, and how of physical AI, we offer a strategic blueprint to achieve superior clinical outcomes, economic sustainability, and enhanced patient satisfaction in an increasingly complex post-pandemic world.The book first delivers a multidimensional look at the why, what, and how of the field. It examines historical evolution and cross-industry success to establish a solid foundation before exploring the current landscape of digital convergence and practical implementation. Second, the author focuses on economic and clinical sustainability. Unlike existing texts that dwell on abstract theory, this work prioritizes the healthcare value proposition. Readers will learn how to leverage AI to reduce workflow bottlenecks and improve patient access while maintaining a model that is financially viable and scalable for any environment. Finally, this book provides a blueprint for co-design and research. We identify critical success factors necessary to develop suitable AI, empowering readers to upskill in health informatics. This feature provides the tools to adapt relevant theories into high-quality research outcomes that stand up to the demands of a maturing digital health domain, ensuring that technology serves the user rather than complicating the care process.This book is designed to benefit graduate and senior-level undergraduate students, non-traditional community-college learners, and certificate-seeking health informatics and public health students. Healthcare stakeholders, including physicians, medical administrators, CEOs, CIOs, consultants, and IT practitioners, will find guidance on designing effective systems. Additionally, governing bodies in Australia, the EU, and the US can leverage its strategies to advance computerized medical records and e-health agendas effectively.