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Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data
Artificial intelligence (AI) has emerged as a transformative force across various domains, revolutionizing the way we perceive and address challenges in healthcare. The convergence of AI and healthcare holds immense promise, offering unprecedented opportunities to enhance medical diagnosis, treatment, and patient care. In today’s world, the intersection of AI and healthcare stands as one of the most promising frontiers for innovation and progress. Artificial Intelligence Transformations for Healthcare Applications: Medical Diagnosis, Treatment, and Patient Care embodies this convergence, offering a comprehensive exploration of how AI is revolutionizing various aspects of healthcare delivery. At its core, this book addresses the urgent need for more effective and efficient healthcare solutions in an increasingly complex and data-rich environment. Covering topics such as chronic disease, image classification, and precision medicine, this book is an essential resource for healthcare professionals, medical researchers, AI and machine learning specialists, healthcare administrators and executives, medical educators and students, biomedical engineers, healthcare IT professionals, policy makers and regulators, academicians, and more.
This book covers a wide range of topics on the role of Artificial Intelligence, Machine Learning, and Big Data for healthcare applications and deals with the ethical issues and concerns associated with it. This book explores the applications in different areas of healthcare and highlights the current research. "Big Data and Artificial Intelligence for Healthcare Applications" covers healthcare big data analytics, mobile health and personalized medicine, clinical trial data management and presents how Artificial Intelligence can be used for early disease diagnosis prediction and prognosis. It also offers some case studies that describes the application of Artificial Intelligence and Machine Learning in healthcare. Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.
Artificial intelligence (AI) has emerged as a transformative force across various domains, revolutionizing the way we perceive and address challenges in healthcare. The convergence of AI and healthcare holds immense promise, offering unprecedented opportunities to enhance medical diagnosis, treatment, and patient care. In today's world, the intersection of AI and healthcare stands as one of the most promising frontiers for innovation and progress. Artificial Intelligence Transformations for Healthcare Applications: Medical Diagnosis, Treatment, and Patient Care embodies this convergence, offering a comprehensive exploration of how AI is revolutionizing various aspects of healthcare delivery. At its core, this book addresses the urgent need for more effective and efficient healthcare solutions in an increasingly complex and data-rich environment. Covering topics such as chronic disease, image classification, and precision medicine, this book is an essential resource for healthcare professionals, medical researchers, AI and machine learning specialists, healthcare administrators and executives, medical educators and students, biomedical engineers, healthcare IT professionals, policy makers and regulators, academicians, and more.
AI is poised to transform every aspect of healthcare, including the way we manage personal health, from customer experience and clinical care to healthcare cost reductions. This practical book is one of the first to describe present and future use cases where AI can help solve pernicious healthcare problems. Kerrie Holley and Siupo Becker provide guidance to help informatics and healthcare leadership create AI strategy and implementation plans for healthcare. With this book, business stakeholders and practitioners will be able to build knowledge, a roadmap, and the confidence to support AIin their organizations—without getting into the weeds of algorithms or open source frameworks. Cowritten by an AI technologist and a medical doctor who leverages AI to solve healthcare’s most difficult challenges, this book covers: The myths and realities of AI, now and in the future Human-centered AI: what it is and how to make it possible Using various AI technologies to go beyond precision medicine How to deliver patient care using the IoT and ambient computing with AI How AI can help reduce waste in healthcare AI strategy and how to identify high-priority AI application
The book Digital Health Transformation with Blockchain and Artificial Intelligence covers the global digital revolution in the field of healthcare sector. The population has been overcoming the COVID-19 period; therefore, we need to establish intelligent digital healthcare systems using various emerging technologies like Blockchain and Artificial Intelligence. Internet of Medical Things is the technological revolution that has included the element of "smartness" in the healthcare industry and also identifying, monitoring, and informing service providers about the patient’s clinical information with faster delivery of care services. This book highlights the important issues i.e. (a) How Internet of things can be integrated with the healthcare ecosystem for better diagnostics, monitoring, and treatment of the patients, (b) Artificial Intelligence for predictive and preventive healthcare systems, (c) Blockchain for managing healthcare data to provide transparency, security, and distributed storage, and (d) Effective remote diagnostics and telemedicine approach for developing smart care. The book encompasses chapters belong to the blockchain, Artificial Intelligence, and Big health data technologies. Features: Blockchain and internet of things in healthcare systems Secure Digital Health Data Management in Internet of Things Public Perception towards AI-Driven Healthcare Security, privacy issues and challenges in adoption of smart digital healthcare Big data analytics and Internet of things in the pandemic era Clinical challenges for digital health revolution Artificial intelligence for advanced healthcare Future Trajectory of Healthcare with Artificial Intelligence 9 Parkinson disease pre-diagnosis using smart technologies Emerging technologies to combat the COVID-19 Machine Learning and Internet of Things in Digital Health Transformation Effective Remote Healthcare and Telemedicine Approaches Legal implication of blockchain technology in public health This Book on "Digital Health Transformation with Blockchain and Artificial Intelligence" aims at promoting and facilitating exchanges of research knowledge and findings across different disciplines on the design and investigation of secured healthcare data analytics. It can also be used as a textbook for a Masters course in security and biomedical engineering. This book will also present new methods for the medical data analytics, blockchain technology, and diagnosis of different diseases to improve the quality of life in general, and better integration into digital healthcare.
Artificial intelligence (AI) is once again in the news, with many major figures urging caution as developments in the technology accelerate. AI impacts all aspects of our lives, but perhaps the discipline of Biomedical Informatics is more affected than most, and is an area where the possible pitfalls of the technology might have particularly serious consequences. This book presents the papers delivered at ICIMTH 2023, the 21st International Conference on Informatics, Management, and Technology in Healthcare, held in Athens, Greece, from 1-3 July 2023. The ICIMTH conferences form a series of scientific events which offers a platform for scientists working in the field of biomedical and health informatics from all continents to gather and exchange research findings and experience. The title of the 2023 conference was Healthcare Transformation with Informatics and Artificial Intelligence, reflecting the importance of AI to healthcare informatics. A total of 252 submissions were received by the Program Committee, of which 149 were accepted as full papers, 13 as short communications, and 14 as poster papers after review. The papers cover a wide range of technologies, and topics include imaging, sensors, biomedical equipment, and management and organizational aspects, as well as legal and social issues. The book provides a timely overview of informatics and technology in healthcare during this time of extremely fast developments, and will be of interest to all those working in the field.
When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment. Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning. This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers’ needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.
A Science Friday pick for book of the year, 2019 One of America's top doctors reveals how AI will empower physicians and revolutionize patient care Medicine has become inhuman, to disastrous effect. The doctor-patient relationship--the heart of medicine--is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard. Innovative, provocative, and hopeful, Deep Medicine shows us how the awesome power of AI can make medicine better, for all the humans involved.
Accelerated Path to Cures provides a transformative perspective on the power of combining advanced computational technologies, modeling, bioinformatics and machine learning approaches with nonclinical and clinical experimentation to accelerate drug development. This book discusses the application of advanced modeling technologies, from target identification and validation to nonclinical studies in animals to Phase 1-3 human clinical trials and post-approval monitoring, as alternative models of drug development. As a case of successful integration of computational modeling and drug development, we discuss the development of oral small molecule therapeutics for inflammatory bowel disease, from the application of docking studies to screening new chemical entities to the development of next-generation in silico human clinical trials from large-scale clinical data. Additionally, this book illustrates how modeling techniques, machine learning, and informatics can be utilized effectively at each stage of drug development to advance the progress towards predictive, preventive, personalized, precision medicine, and thus provide a successful framework for Path to Cures.