Artificial Intelligence in Medicine - AI eBooks

A fresh look at a “robot-proof” education in the new age of generative AI.In 2017, Robot-Proof, the first edition, foresaw the advent of the AI economy and called for a new model of higher education designed to help human beings flourish alongside smart machines. That economy has arrived.Aoun puts forth a framework for a new curriculum, humanics, which integrates technological, data, and human literacies in an experiential setting, and he renews the call for universities to embrace lifelong learning through a social compact with government, employers, and learners themselves. Drawing on the latest developments and debates around generative AI, Robot-Proof is a blueprint for the university as a force for human reinvention in an era of technological change—an era in which we must constantly renegotiate the shifting boundaries between artificial intelligence and the capacities that remain uniquely human.

From two of TIME's 100 Most Influential People in AI, what you need to know about AI--and how to defend yourself against bogus AI claims and products. Confused about AI and worried about what it means for your future and the future of the world? You're not alone. AI is everywhere--and few things are surrounded by so much hype, misinformation, and misunderstanding. In AI Snake Oil, computer scientists Arvind Narayanan and Sayash Kapoor cut through the confusion to give you an essential understanding of how AI works and why it often doesn't, where it might be useful or harmful, and when you should suspect that companies are using AI hype to sell AI snake oil--products that don't work, and probably never will. While acknowledging the potential of some AI, such as ChatGPT, AI Snake Oil uncovers rampant misleading claims about the capabilities of AI and describes the serious harms AI is already causing in how it's being built, marketed, and used in areas such as education, medicine, hiring, banking, insurance, and criminal justice. The book explains the crucial differences between types of AI, why organizations are falling for AI snake oil, why AI can't fix social media, why AI isn't an existential risk, and why we should be far more worried about what people will do with AI than about anything AI will do on its own. The book also warns of the dangers of a world where AI continues to be controlled by largely unaccountable big tech companies

Using Generative AI Effectively in Higher Education explores how higher education providers can realise their role and responsibility in harnessing the power of generative artificial intelligence (GenAI) ethically and sustainably. This rich collection of established and evaluated practices from across global higher education offers a practical guide to leading an agile institutional response to emerging technologies, building critical digital literacy across an entire institution, and embedding the ethical and sustainable use of GenAI in teaching, learning, and assessment. Including reflections from stakeholders testifying to the value of the approaches outlined, the book examines how higher education can equip staff and students with the critical-digital literacy necessary to use GenAI in work, study, and social life responsibly and with integrity. It provides an evidence-based resource for any kind of higher education (HE) provider (modern, college-based, and research-focused) looking for inspiration and approaches which can build GenAI capability and includes chapters on the development of cross-institutional strategy, policies and processes, pedagogic practices, and critical-digital literacy. This resource will be invaluable to educational leaders, educational developers, learning developers, learning technologists, course administrators, quality assurance staff, and HE teachers wishing to embrace and adapt to a GenAI-enabled world.

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

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.

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

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.

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.

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.

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.

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.

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.

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.

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

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

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.