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 covers wide areas of animal and human psychopharmacology with clinical utility in the treatment of psychiatric and neurological (e.g Alzheimer's disease) disorders. The main theme is to develop a new paradigm for drug discovery that questions the claim that animal models or assays fail adequately to predict Phase 3 clinical trials. A new paradigm is advocated that stresses the importance of intermediate staging points between these extremes that depend on suitable translation of findings from animal studies to Phase 1 or Phase 2 studies utilising experimental medicine.

This book reviews the application of artificial intelligence and machine learning in healthcare. It discusses integrating the principles of computer science, life science, and statistics incorporated into statistical models using existing data, discovering patterns in data to extract the information, and predicting the changes and diseases based on this data and models. The initial chapters of the book cover the practical applications of artificial intelligence for disease prognosis & management. Further, the role of artificial intelligence and machine learning is discussed with reference to specific diseases like diabetes mellitus, cancer, mycobacterium tuberculosis, and Covid-19. The chapters provide working examples on how different types of healthcare data can be used to develop models and predict diseases using machine learning and artificial intelligence. The book also touches upon precision medicine, personalized medicine, and transfer learning, with the real examples. Further, it also discusses the use of machine learning and artificial intelligence for visualization, prediction, detection, and diagnosis of Covid -19. This book is a valuable source of information for programmers, healthcare professionals, and researchers interested in understanding the applications of artificial intelligence and machine learning in healthcare.

Provides a definitive overview of the complex ecosystem facilitating Alzheimer's Disease drug research and development. Demonstrates a drug's journey from in the lab, clinical trial testing, regulatory review, and marketing by pharmaceutical companies. Details the use of artificial intelligence, clinical trial management, and financing models.

Although there are several gaps in understanding the many issues related to neurological disorders, we know enough to be able to shape effective policy responses to some of the most common. This book describes and discusses the increasing public health impact of common neurological disorders such as dementia, epilepsy, headache disorders, multiple sclerosis, neuroinfections, neurological disorders associated with malnutrition, pain associated with neurological disorders, Parkinson's disease, stroke and traumatic brain injuries. It provides information and advice on public health interventions that may reduce their occurrence and consequences, and offers health professionals and planners the opportunity to assess the burden caused by these disorders. The clear message that emerges is that unless immediate action is taken globally, the neurological burden is likely to become an increasingly serious and unmanageable.

As the largest generation in U.S. history - the population born in the two decades immediately following World War II - enters the age of risk for cognitive impairment, growing numbers of people will experience dementia (including Alzheimer's disease and related dementias). By one estimate, nearly 14 million people in the United States will be living with dementia by 2060. Like other hardships, the experience of living with dementia can bring unexpected moments of intimacy, growth, and compassion, but these diseases also affect people's capacity to work and carry out other activities and alter their relationships with loved ones, friends, and coworkers. Those who live with and care for individuals experiencing these diseases face challenges that include physical and emotional stress, difficult changes and losses in their relationships with life partners, loss of income, and interrupted connections to other activities and friends. From a societal perspective, these diseases place substantial demands on communities and on the institutions and government entities that support people living with dementia and their families, including the health care system, the providers of direct care, and others. Nevertheless, research in the social and behavioral sciences points to possibilities for preventing or slowing the development of dementia and for substantially reducing its social and economic impacts. At the request of the National Institute on Aging of the U.S. Department of Health and Human Services, Reducing the Impact of Dementia in America assesses the contributions of research in the social and behavioral sciences and identifies a research agenda for the coming decade. This report offers a blueprint for the next decade of behavioral and social science research to reduce the negative impact of dementia for America's diverse population. Reducing the Impact of Dementia in America calls for research that addresses the causes and solutions for disparities in both developing dementia and receiving adequate treatment and support. It calls for research that sets goals meaningful not just for scientists but for people living with dementia and those who support them as well. By 2030, an estimated 8.5 million Americans will have Alzheimer's disease and many more will have other forms of dementia. Through identifying priorities social and behavioral science research and recommending ways in which they can be pursued in a coordinated fashion, Reducing the Impact of Dementia in America will help produce research that improves the lives of all those affected by dementia.