THE SERIES: INTELLIGENT BIOMEDICAL DATA ANALYSIS By focusing on the methods and tools for intelligent data analysis, this series aims to narrow the increasing gap between data gathering and data comprehension. Emphasis is also given to the problems resulting from automated data collection in modern hospitals, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring. In medicine, overcoming this gap is crucial since medical decision making needs to be supported by arguments based on existing medical knowledge as well as information, regularities and trends extracted from big data sets.

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

This book collects research works of data-driven medical diagnosis done via Artificial Intelligence based solutions, such as Machine Learning, Deep Learning and Intelligent Optimization. Physical devices powered with Artificial Intelligence are gaining importance in diagnosis and healthcare. Medical data from different sources can also be analyzed via Artificial Intelligence techniques for more effective results.

Health care utilization routinely generates vast amounts of data from sources ranging from electronic medical records, insurance claims, vital signs, and patient-reported outcomes. Predicting health outcomes using data modeling approaches is an emerging field that can reveal important insights into disproportionate spending patterns. This book presents data driven methods, especially machine learning, for understanding and approaching the high utilizers problem, using the example of a large public insurance program. It describes important goals for data driven approaches from different aspects of the high utilizer problem, and identifies challenges uniquely posed by this problem. Key Features: Introduces basic elements of health care data, especially for administrative claims data, including disease code, procedure codes, and drug codes Provides tailored supervised and unsupervised machine learning approaches for understanding and predicting the high utilizers Presents descriptive data driven methods for the high utilizer population Identifies a best-fitting linear and tree-based regression model to account for patients’ acute and chronic condition loads and demographic characteristics

Academic scholars and professionals are currently grappling with hurdles in optimizing diagnostic processes, as traditional methodologies prove insufficient in managing the intricate and voluminous nature of medical data. The diverse range of imaging techniques, spanning from endoscopy to magnetic resonance imaging, necessitates a more unified and efficient approach. This complexity has created a pressing need for streamlined methodologies and innovative solutions. Academic scholars find themselves at the forefront of addressing these challenges, seeking ways to leverage AI's full potential in improving the accuracy of medical imaging diagnostics and, consequently, enhancing overall patient outcomes. Future of AI in Medical Imaging, stands as a solution to the challenges faced by academic scholars in the realm of medical imaging. The book lays a solid groundwork for understanding the complexities of medical imaging systems. Through an exploration of various imaging modalities, it not only addresses the current issues but also serves as a guide for scholars to navigate the landscape of AI-integrated medical diagnostics. This collaborative effort not only illuminates the existing hurdles of medical imaging but also looks towards a future where AI-driven diagnostics and personalized medicine become indispensable tools, significantly elevating patient outcomes.

This book provides a structured and analytical guide to the use of artificial intelligence in medicine. Covering all areas within medicine, the chapters give a systemic review of the history, scientific foundations, present advances, potential trends, and future challenges of artificial intelligence within a healthcare setting. Artificial Intelligence in Medicine aims to give readers the required knowledge to apply artificial intelligence to clinical practice. The book is relevant to medical students, specialist doctors, and researchers whose work will be affected by artificial intelligence.

Introducing "The Data-Driven Doctor: How AI is Transforming Medicine" Your Essential Guide to the Future of Healthcare! Embark on a groundbreaking journey into the world of artificial intelligence (AI) and medicine with "The Data-Driven Doctor." In this captivating book, you'll discover the transformative power of AI in reshaping the future of healthcare delivery, clinical decision-making, and patient care. From early disease detection to personalized treatment approaches, AI is revolutionizing every aspect of medicine, offering unprecedented opportunities to improve patient outcomes, enhance clinical efficiency, and drive innovation in healthcare. Through real-world examples, cutting-edge research, and expert insights, "The Data-Driven Doctor" explores the latest advancements in AI technology and their profound implications for the practice of medicine. Whether you're a healthcare professional seeking to stay ahead of the curve, a patient curious about the future of healthcare, or an AI enthusiast eager to learn about its transformative potential, this book is your indispensable guide to navigating the dynamic intersection of AI and medicine. Discover how AI-driven diagnostic tools are revolutionizing disease detection, how personalized medicine is transforming treatment paradigms, and how AI-powered clinical decision support systems are empowering healthcare providers to deliver more precise, patient-centered care than ever before. Explore the ethical, social, and regulatory considerations surrounding AI use in medicine, and gain valuable insights into the future of healthcare delivery in the digital age. Packed with fascinating anecdotes, thought-provoking insights, and actionable takeaways, "The Data-Driven Doctor" is a must-read for anyone passionate about the future of healthcare and the transformative potential of AI. Whether you're a seasoned healthcare professional, a curious patient, or an AI enthusiast, this book will inspire and inform, guiding you on an enlightening journey into the future of medicine. Don't miss out on your chance to explore the future of healthcare with "The Data-Driven Doctor." Order your copy today and join the revolution in AI-driven medicine!