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Author: Marcelo Corrales Compagnucci
Publisher: Cambridge University Press
ISBN: 1108924859
Category : Law
Languages : en
Pages : 493
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Book Description
The emergence of digital platforms and the new application economy are transforming healthcare and creating new opportunities and risks for all stakeholders in the medical ecosystem. Many of these developments rely heavily on data and AI algorithms to prevent, diagnose, treat, and monitor diseases and other health conditions. A broad range of medical, ethical and legal knowledge is now required to navigate this highly complex and fast-changing space. This collection brings together scholars from medicine and law, but also ethics, management, philosophy, and computer science, to examine current and future technological, policy and regulatory issues. In particular, the book addresses the challenge of integrating data protection and privacy concerns into the design of emerging healthcare products and services. With a number of comparative case studies, the book offers a high-level, global, and interdisciplinary perspective on the normative and policy dilemmas raised by the proliferation of information technologies in a healthcare context.
Author: Marcelo Corrales Compagnucci
Publisher: Cambridge University Press
ISBN: 1108924859
Category : Law
Languages : en
Pages : 493
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Book Description
The emergence of digital platforms and the new application economy are transforming healthcare and creating new opportunities and risks for all stakeholders in the medical ecosystem. Many of these developments rely heavily on data and AI algorithms to prevent, diagnose, treat, and monitor diseases and other health conditions. A broad range of medical, ethical and legal knowledge is now required to navigate this highly complex and fast-changing space. This collection brings together scholars from medicine and law, but also ethics, management, philosophy, and computer science, to examine current and future technological, policy and regulatory issues. In particular, the book addresses the challenge of integrating data protection and privacy concerns into the design of emerging healthcare products and services. With a number of comparative case studies, the book offers a high-level, global, and interdisciplinary perspective on the normative and policy dilemmas raised by the proliferation of information technologies in a healthcare context.
Author: Surbhi Bhatia
Publisher: Springer Nature
ISBN: 3030670511
Category : Technology & Engineering
Languages : en
Pages : 323
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Book Description
This book fosters a scientific debate for sophisticated approaches and cognitive technologies (such as deep learning, machine learning and advanced analytics) for enhanced healthcare services in light of the tremendous scope in the future of intelligent systems for healthcare. The authors discuss the proliferation of huge data sources (e.g. genomes, electronic health records (EHRs), mobile diagnostics, and wearable devices) and breakthroughs in artificial intelligence applications, which have unlocked the doors for diagnosing and treating multitudes of rare diseases. The contributors show how the widespread adoption of intelligent health based systems could help overcome challenges, such as shortages of staff and supplies, accessibility barriers, lack of awareness on certain health issues, identification of patient needs, and early detection and diagnosis of illnesses. This book is a small yet significant step towards exploring recent advances, disseminating state-of-the-art techniques and deploying novel technologies in intelligent healthcare services and applications. Describes the advances of computing methodologies for life and medical science data; Presents applications of artificial intelligence in healthcare along with case studies and datasets; Provides an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians.
Author: Arman Kilic
Publisher: Academic Press
ISBN: 012822519X
Category : Medical
Languages : en
Pages : 0
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Book Description
Artificial Intelligence and Machine Learning in Healthcare discusses the potential of groundbreaking technologies on the delivery of care. A lot have been said about how artificial intelligence and machine learning can improve healthcare, however there are still many doubts and concerns among health professionals, all of which are addressed in this book. Sections cover History and Basic Overview of AI and ML, with differentiation of supervised, unsupervised and deep learning, Applications of AI and ML in Healthcare, The Future of Healthcare with AI, Challenges to Adopting AI in Healthcare, and ethics and legal processes for implementation.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. - Provides an overview of AI and ML to the medical practitioner who may not be well versed in these fields - Encompasses a thorough review of what has been accomplished and demonstrated recently in the fields of AI and ML in healthcare - Discusses the future of AI and ML in healthcare, with a review of possible wearable technology and software and how they may be used for medical care
Author: Arash Shaban-Nejad
Publisher: Springer Nature
ISBN: 3030533522
Category : Technology & Engineering
Languages : en
Pages : 344
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Book Description
This book highlights the latest advances in the application of artificial intelligence and data science in health care and medicine. Featuring selected papers from the 2020 Health Intelligence Workshop, held as part of the Association for the Advancement of Artificial Intelligence (AAAI) Annual Conference, it offers an overview of the issues, challenges, and opportunities in the field, along with the latest research findings. Discussing a wide range of practical applications, it makes the emerging topics of digital health and explainable AI in health care and medicine accessible to a broad readership. The availability of explainable and interpretable models is a first step toward building a culture of transparency and accountability in health care. As such, this book provides information for scientists, researchers, students, industry professionals, public health agencies, and NGOs interested in the theory and practice of computational models of public and personalized health intelligence.
Author: Tom Lawry
Publisher: CRC Press
ISBN: 1000036324
Category : Business & Economics
Languages : en
Pages : 198
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Book Description
We are in the early stages of the next big platform shift in healthcare computing. Fueled by Artificial Intelligence (AI) and the Cloud, this shift is already transforming the way health and medical services are provided. As the industry transitions from static digital repositories to intelligent systems, there will be winners and losers in the race to innovate and automate the provision of services. Critical to success will be the role leaders play in shaping the use of AI to be less "artificial" and more "intelligent" in support of improving processes to deliver care and keep people healthy and productive across all care settings. This book defines key technical, process, people, and ethical issues that need to be understood and addressed in successfully planning and executing an enterprise-wide AI plan. It provides clinical and business leaders with a framework for moving organizations from the aspiration to execution of intelligent systems to improve clinical, operational, and financial performance.
Author: Chee-Peng Lim
Publisher: Springer Nature
ISBN: 3031111540
Category : Technology & Engineering
Languages : en
Pages : 239
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Book Description
Artificial intelligence (AI) and machine learning (ML) have transformed many standard and conventional methods in undertaking health and well-being issues of humans. AL/ML-based systems and tools play a critical role in this digital and big data era to address a variety of medical and healthcare problems, improving treatments and quality of care for patients. This edition on AI and ML for healthcare consists of two volumes. The first presents selected AI and ML studies on medical imaging and healthcare data analytics, while the second unveils emerging methodologies and trends in AI and ML for delivering better medical treatments and healthcare services in the future. In this first volume, progresses in AI and ML technologies for medical image, video, and signal processing as well as health information and data analytics are presented. These selected studies offer readers theoretical and practical knowledge and ideas pertaining to recent advances in AI and ML for effective and efficient image and data analytics, leading to state-of-the-art AI and ML technologies for advancing the healthcare sector.
Author: Arjun Panesar
Publisher: Apress
ISBN: 1484237994
Category : Computers
Languages : en
Pages : 390
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Book Description
Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll LearnGain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agentsWho This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.
Author: Lilhore, Umesh Kumar
Publisher: IGI Global
ISBN:
Category : Medical
Languages : en
Pages : 373
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Book Description
The use of artificial intelligence (AI) in data-driven medicine has revolutionized healthcare, presenting practitioners with unprecedented tools for diagnosis and personalized therapy. However, this progress comes with a critical concern: the security and privacy of sensitive patient data. As healthcare increasingly leans on AI, the need for robust solutions to safeguard patient information has become more pressing than ever. Federated Learning and Privacy-Preserving in Healthcare AI emerges as the definitive solution to balancing medical progress with patient data security. This carefully curated volume not only outlines the challenges of federated learning but also provides a roadmap for implementing privacy-preserving AI systems in healthcare. By decentralizing the training of AI models, federated learning mitigates the risks associated with centralizing patient data, ensuring that critical information never leaves its original location. Aimed at healthcare professionals, AI experts, policymakers, and academics, this book not only delves into the technical aspects of federated learning but also fosters a collaborative approach to address the multifaceted challenges at the intersection of healthcare and AI.
Author: Janak Gunatilleke
Publisher:
ISBN: 9781739637408
Category : Business & Economics
Languages : en
Pages : 0
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Book Description
Do you want to learn about how AI can and can't help improve healthcare? Do you want to learn how to improve the adoption of AI in healthcare? AI is often seen as a silver bullet, but in many instances AI and data based solutions in healthcare don't fully address the problem and are sometimes not clinically safe. The current adoption of AI in healthcare is at best ad hoc, with a large number of solutions stuck in the proof of concept stage, limited to certain specialities or without robust evidence of their effectiveness. Healthcare has always faced significant challenges, only increased by unprecedented events related to the Covid-19 pandemic. AI solutions have been proven to work and add value in other industries, and it has potential to do the same in healthcare and to help address issues which face the industry. While there are parallels between healthcare and other industries with respect to the implementation of AI solutions, the healthcare industry is unique. The nature of healthcare, and its impact on people's lives, makes certain factors more important than in other industries. Existing guidance and manuals provide good insights, however, you have to refer to many different sources, and attempt to 'knit it all together'. This book presents you with a more holistic approach, which improves the likelihood of a successful implementation and, later, scalability; an approach which considers both the solution lifecycle and the stakeholders involved in it. This book also highlights certain enablers which need support, planning and funding at a national level. It expands on the technical and academic style of previous resources to provide you with an accessible reference guide that is practitioner and implementation focused, grounded in evidence, and brought to life by real life examples and expert interviews. By the end of this book, you will: Understand the potential of AI to add value in healthcare and to improve patient outcomes Know where AI implementation has worked, and the lessons learned from where it hasn't Have a new approach to consider when designing, selecting, and deploying AI solutions in healthcare to increase the likelihood of success and adoption at scale
Author: Ajay Agrawal
Publisher: University of Chicago Press
ISBN: 0226833127
Category : Business & Economics
Languages : en
Pages : 172
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Book Description
A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
