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
In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help.
This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges.
This book will be of use to mental health practitioners interested in learning about, or incorporating AI advances into their practice and for researchers interested in a comprehensive review of these advances in one source.
This book discusses the different types of AI applicable to healthcare and their application in medicine, population health, genomics, healthcare administration, and delivery.
... James A. Brickley and Clifford W. Smith Jr U.S. Federal Securities Law Thomas Lee Hazen Cybersecurity Law David P. Fidler The Sociology of Work Amy S. Wharton Marketing Strategy George S. Day Scenario Planning Paul Schoemaker Financial.
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.
J.F. Hair, G.T.M. Hult, C.M. Ringle, M. Sarstedt, N.F. Richter, S. Hauff, Partial Least Squares Strukturgleichungsmodellierung (Eine anwendungsorientierte Einführung. Verlag Franz Vahlen, München, 2017) 94. W.W. Chin, The partial least ...
This book offers a comprehensive yet concise overview of the challenges and opportunities presented by the use of artificial intelligence in healthcare.
The book is intended for clinical researchers and computational scientists seeking to understand the recent advances of AI in health and medicine. Many universities may use the book as a secondary training text.
This book is a valuable resource for bioinformaticians, clinicians, graduate students and several members of biomedical field who needs to get up to speed on the revolutionary role of AI and Machine Learning in healthcare.