The integration of Artificial Intelligence (AI) into the realm of healthcare has ushered in a new era of possibilities, redefining the way we diagnose, treat, and manage diseases. This book is a journey into the convergence of these two dynamic fields, aimed at unveiling the transformative power of AI in revolutionizing healthcare delivery and outcomes. From the laboratories to the clinics, AI has emerged as a catalyst of innovation, sparking advances that were once relegated to the realm of science fiction. In this era of unprecedented data availability and computing prowess, AI offers a novel lens through which we can explore and comprehend the intricacies of health and disease. As the pages unfold, readers will embark on an exploration of the myriad ways AI is redefining healthcare—from predictive analytics and personalized medicine to image analysis and patient engagement. Each chapter is a gateway into a different facet of this multidimensional landscape, delving deep into the methodologies, applications, and implications that underpin AI's integration into healthcare systems. Charting the Path Ahead The preface sets the stage for the captivating journey that follows. We introduce the readers to the burgeoning landscape of AI in healthcare, underscoring the significance of this intersection and its potential to reshape the future of medicine. As technology and medical science march hand in hand, AI emerges as the bridge that connects innovation with real-world impact. It is the culmination of data-driven insights and algorithmic precision that holds the promise of advancing patient care, streamlining clinical workflows, and propelling medical research to new horizons. Traversing the Landscape of AI and Healthcare As you delve into each chapter, you'll find a comprehensive exploration of AI's applications in healthcare. From the fundamentals of machine learning to the complexities of predictive analytics and the ethical considerations that underscore the AI revolution, every aspect is carefully dissected. The book is a testament to the collaborative efforts of professionals, researchers, and thought leaders who have harnessed their expertise to unravel the potentials and pitfalls of AI-driven healthcare. Guiding the Way This book is not only an informative companion but also a guiding light for those navigating the uncharted waters of AI in healthcare. Whether you're a seasoned healthcare practitioner, a tech enthusiast, or a curious mind seeking to grasp the intricate details of this paradigm shift, you'll find a wealth of knowledge that equips you with insights and tools for meaningful engagement. Conclusion This book invites you to embark on a journey of discovery, innovation, and transformation. As AI continues to weave its way into the fabric of healthcare, its implications are far-reaching and profound. With this book as your guide, you'll be equipped to traverse the exciting landscape of AI-driven healthcare, gaining insights that will empower you to harness the power of technology in the service of human health and well-being.

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

Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll LearnGain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agentsWho This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.

In this book, the author examines the ethical implications of Artificial Intelligence systems as they integrate and replace traditional social structures in new sociocognitive-technological environments. She discusses issues related to the integrity of researchers, technologists, and manufacturers as they design, construct, use, and manage artificially intelligent systems; formalisms for reasoning about moral decisions as part of the behavior of artificial autonomous systems such as agents and robots; and design methodologies for social agents based on societal, moral, and legal values. Throughout the book the author discusses related work, conscious of both classical, philosophical treatments of ethical issues and the implications in modern, algorithmic systems, and she combines regular references and footnotes with suggestions for further reading. This short overview is suitable for undergraduate students, in both technical and non-technical courses, and for interested and concerned researchers, practitioners, and citizens.

Precision Medicine and Artificial Intelligence: The Perfect Fit for Autoimmunity covers background on artificial intelligence (AI), its link to precision medicine (PM), and examples of AI in healthcare, especially autoimmunity. The book highlights future perspectives and potential directions as AI has gained significant attention during the past decade. Autoimmune diseases are complex and heterogeneous conditions, but exciting new developments and implementation tactics surrounding automated systems have enabled the generation of large datasets, making autoimmunity an ideal target for AI and precision medicine. More and more diagnostic products utilize AI, which is also starting to be supported by regulatory agencies such as the Food and Drug Administration (FDA). Knowledge generation by leveraging large datasets including demographic, environmental, clinical and biomarker data has the potential to not only impact the diagnosis of patients, but also disease prediction, prognosis and treatment options. - Allows the readers to gain an overview on precision medicine for autoimmune diseases leveraging AI solutions - Provides background, milestone and examples of precision medicine - Outlines the paradigm shift towards precision medicine driven by value-based systems - Discusses future applications of precision medicine research using AI - Other aspects covered in the book include regulatory insights, data analytics and visualization, types of biomarkers as well as the role of the patient in precision medicine

This book reviews key recent advances and new frontiers within psychiatric research and clinical practice. These advances either represent or are enabling paradigm shifts in the discipline and are influencing how we observe, derive and test hypotheses, and intervene. Progress in information technology is allowing the collection of scattered, fragmented data and the discovery of hidden meanings from stored data, and the impacts on psychiatry are fully explored. Detailed attention is also paid to the applications of artificial intelligence, machine learning, and data science technology in psychiatry and to their role in the development of new hypotheses, which in turn promise to lead to new discoveries and treatments. Emerging research methods for precision medicine are discussed, as are a variety of novel theoretical frameworks for research, such as theoretical psychiatry, the developmental approach to the definition of psychopathology, and the theory of constructed emotion. The concluding section considers novel interventions and treatment avenues, including psychobiotics, the use of neuromodulation to augment cognitive control of emotion, and the role of the telomere-telomerase system in psychopharmacological interventions.

Topic Editor H. Peter Soyer is a shareholder of MoleMap NZ Limited and e-derm consult GmbH, and undertakes regular teledermatological reporting for both companies. He is a Medical Consultant for Canfield Scientific Inc., MetaOptima and Revenio Research Oy and also a Medical Advisor for First Derm.