Artificial Intelligence in Medicine - AI eBooks

Compiles current research on Artificial Intelligence within medical subspecialties, helping practitioners with diagnosis, clinical decision-making, disease prediction, prevention, and the facilitation of precision medicine.

Applications of Artificial Intelligence in Healthcare and Biomedicine provides updated knowledge on the applications of artificial intelligence in medical image analysis. In 16 chapters, it presents artificial applications in Electrocardiogram (ECG), Electroencephalogram (EEG) and Electromyography (EMG), signal analysis, Computed Tomography (CT), Magnetic Resonance Imaging (MR), and Ultrasound image analysis.

This reference text presents the usage of artificial intelligence in healthcare and discusses the challenges and solutions of using advanced techniques like wearable technologies and image processing in the sector.

Highlights different approaches, theories, and applications of intelligent systems from a practical as well as a theoretical view of the healthcare domain.

This book aims to present the impact of Artificial Intelligence (AI) and Big Data in healthcare for medical decision making and data analysis in myriad fields including Radiology, Radiomics, Radiogenomics, Oncology, Pharmacology, COVID-19 prognosis, Cardiac imaging, Neuroradiology, Psychiatry and others.

Aimed at those with a statistical background who want to use their strengths in pursuing AI research the book covers broad AI topics in drug development, precision medicine, and healthcare.

Describes where AI is currently being used to change practice, and provides successful cases of AI approaches in specific medical domains.

Covers governance of ethics, bias, and privacy. Shows how delivery of patient-centered care improves patient outcomes at scale.

Provides a global perspective on AI and the challenges it represents, and will focus on the digital ethics surrounding AI technology.

In this original work, Tom Lawry takes readers on a journey of understanding what we learned from fighting a global pandemic and how to apply these learnings to solve healthcare's other big challenges. This book is about empowering clinicians and consumers alike to take control of what is important to them by harnessing the power of AI and the Intelligent Health Revolution to create a sustainable system that focuses on keeping all citizens healthy while caring for them when they are not.

This book aims to highlight the latest achievements in the use of AI in personalized medicine and healthcare delivery. The edited book contains selected papers presented at the 2023 Health Intelligence workshop, co-located with the Thirty-Seven Association for the Advancement of Artificial Intelligence (AAAI) conference, and presents an overview of the issues, challenges, and potentials in the field, along with new research results. This book provides information for researchers, students, industry professionals, clinicians, and public health agencies interested in the applications of AI in medicine and public health.

Healthcare transformation requires us to continually look at new and better ways to manage insights - both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization's day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business.

Artificial Intelligence for Improved Patient Outcomes provides new, relevant, and practical information on what AI can do in healthcare and how to assess whether AI is improving health outcomes. With clear insights and a balanced approach, this innovative book offers a one-stop guide on how to design and lead pragmatic real-world AI studies that yield rigorous scientific evidence--all in a manner that is safe and ethical.