Artificial intelligence (AI) / Inteligencia Artificial (IA): Una Guía

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.

Handbook of AI-Based Models in Healthcare and Medicine: Approaches, Theories, and Applications highlights different approaches, theories, and applications of intelligent systems from a practical as well as a theoretical view of the healthcare domain. It uses a medically oriented approach in its discussions of human biology, healthcare, and medicine and presents NLP-based medical reports and medicine enhancements. The handbook includes advanced models of ML and DL for the management of healthcare systems and also discusses blockchain-based healthcare management. In addition, the handbook offers use cases where AI, ML, and DL can help solve healthcare complications.

Artificial Intelligence in Clinical Practice: How AI Technologies Impact Medical Research and Clinics 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. The book defines the basic concepts of big data and AI in medicine and highlights current applications, challenges, ethical issues, and biases. Each chapter discusses AI applied to a specific medical subspecialty, including primary care, preventive medicine, general internal medicine, radiology, pathology, infectious disease, gastroenterology, cardiology, hematology, oncology, dermatology, ophthalmology, mental health, neurology, pulmonary, critical care, rheumatology, surgery, and OB-GYN.

From improving the diagnosis, treatment, and monitoring of many medical conditions to the effective implementation of precise patient care, this book will help you understand datasets produced from digital health technologies and IoT and teach you how to employ analytical methods such as convolutional neural networks and deep learning to analyze that data. You’ll also see how this data-driven approach can enhance and democratize value-based healthcare delivery. Additionally, you’ll learn how the convergence of AI and precision health is revolutionizing healthcare, including some of the most difficult challenges facing precision medicine, such as ethics, bias, privacy, and health equity.

This open access book provides a detailed review of the latest methods and applications of artificial intelligence (AI) and machine learning (ML) in medicine. With chapters focusing on enabling the reader to develop a thorough understanding of the key concepts in these subject areas along with a range of methods and resulting models that can be utilized to solve healthcare problems, the use of causal and predictive models are comprehensively discussed. Care is taken to systematically describe the concepts to facilitate the reader in developing a thorough conceptual understanding of how different methods and resulting models function and how these relate to their applicability to various issues in health care and medical sciences. Guidance is also given on how to avoid pitfalls that can be encountered on a day-to-day basis and stratify potential clinical risks.

The doctor-patient relationship--the heart of medicine--is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine , leading physician Eric Topol reveals how artificial intelligence can help. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard. Innovative, provocative, and hopeful, Deep Medicine shows us how the awesome power of AI can make medicine better, for all the humans involved.

The emergence of artificial intelligence (AI) as a tool for better health care offers unprecedented
opportunities to improve patient and clinical team outcomes, reduce costs, and impact population
health. Examples include but are not limited to automation; providing patient, “fRamily” (friends and
family unpaid caregivers), and health care professionals’ information synthesis; and recommendations
and visualization of information for shared decision making.

This book focuses on this vision of universalization by dealing with the development and framework of AI applicable to all. It develops a moral framework based on a neo-Darwinian approach - the concept of Ethics by Evolution - to accompany AI by observing a certain number of requirements, recommendations and rules at each stage of design, implementation and use. The societal responsibility of artificial intelligence is an essential step towards ethical, eco-responsible and trustworthy AI, aiming to protect and serve people and the common good in a beneficial way.

New technological advancements have always changed the way society and human relationships work. New affordances created by technological tools inevitable modify and affect the way people interact with such tools, as well as with one another, and with the world within which this technology is embedded. Technology, Users and Uses explores and discusses ethical issues around the use of technology and AI, by focusing on the way they affect individual, social and global interactions.

Crawford reveals how this planetary network is fueling a shift toward undemocratic governance and increased inequity. Rather than taking a narrow focus on code and algorithms, Crawford offers us a material and political perspective on what it takes to make AI and how it centralizes power. This is an urgent account of what is at stake as technology companies use artificial intelligence to reshape the world.

April G. Dawson explores the pros and cons of using powerful AI tools in law school while maintaining institutional standards for academic rigor and intellectual honesty in legal education—alongside students’ own values and developing sense of legal ethics.

The recent advent of more sophisticated AI systems, which function with partial or full autonomy and are capable of tasks which require learning and 'intelligence', presents difficult ethical questions, and has drawn concerns from many quarters about individual and societal welfare, democratic decision-making, moral agency, and the prevention of harm. This work ranges from explorations of normative constraints on specific applications of machine learning algorithms today-in everyday medical practice, for instance-to reflections on the (potential) status of AI as a form of consciousness with attendant rights and duties and, more generally still, on the conceptual terms and frameworks necessarily to understand tasks requiring intelligence, whether "human" or "A.I."

The Ethics of Artificial Intelligence in Education identifies and confronts key ethical issues generated over years of AI research, development, and deployment in learning contexts. Adaptive, automated, and data-driven education systems are increasingly being implemented in universities, schools, and corporate training worldwide, but the ethical consequences of engaging with these technologies remain unexplored. Featuring expert perspectives from inside and outside the AIED scholarly community, this book provides AI researchers, learning scientists, educational technologists, and others with questions, frameworks, guidelines, policies, and regulations to ensure the positive impact of artificial intelligence in learning.

Nexus looks through the long lens of human history to consider how the flow of information has shaped us, and our world. Taking us from the Stone Age, through the canonization of the Bible, early modern witch-hunts, Stalinism, Nazism, and the resurgence of populism today, Yuval Noah Harari asks us to consider the complex relationship between information and truth, bureaucracy and mythology, wisdom and power. He explores how different societies and political systems throughout history have wielded information to achieve their goals, for good and ill. And he addresses the urgent choices we face as non-human intelligence threatens our very existence.

Artificial Intelligence is your guide to the theory and practice of modern AI. It introduces major concepts using intuitive explanations and nontechnical language, before going into mathematical or algorithmic details. In-depth coverage of both basic and advanced topics provides you with a solid understanding of the frontiers of AI without compromising complexity and depth. A unified approach to AI clearly details how the various subfields of AI fit together to build actual, useful programs.

In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent―really―are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go.