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Clinical Practice and Unmet Challenges in AI-Enhanced Healthcare Systems

Clinical Practice and Unmet Challenges in AI-Enhanced Healthcare Systems
Author: Liu, Haipeng
Publisher: IGI Global
Total Pages: 407
Release: 2024-08-05
Genre: Medical
ISBN:
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As the demand for advanced technologies to revolutionize patient care intensifies, the medical industry faces a pressing need to confront challenges hindering the assimilation of AI-enhanced healthcare systems. Issues such as data interoperability, ethical considerations, and the translation of AI advancements into practical clinical applications pose formidable hurdles that demand immediate attention. It is within this context of challenges and opportunities that the book, Clinical Practice and Unmet Challenges in AI-Enhanced Healthcare Systems promises to pave the way for a transformative era in healthcare. The book serves as a comprehensive guide for academic scholars, researchers, and healthcare professionals navigating the dynamic landscape of data-driven, AI-enhanced healthcare. By showcasing the latest advancements, the book empowers its readers to not only comprehend the existing frontiers in data sciences and healthcare technologies but also to actively contribute to overcoming obstacles. Through detailed case studies and practical guidance, the publication equips its audience with the skills necessary to implement AI in various clinical settings.

Artificial Intelligence for Health 4.0: Challenges and Applications

Artificial Intelligence for Health 4.0: Challenges and Applications
Author: Rishabha Malviya
Publisher: CRC Press
Total Pages: 420
Release: 2023-03-10
Genre: Computers
ISBN: 1000847020
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Healthcare is one of the major success stories of our times. Medical science has improved rapidly, raising life expectancy around the world. However, as longevity increases, healthcare systems face growing demands for their services, rising costs, and a workforce that is struggling to meet the needs of its patients. Healthcare is one of the most critical sectors in the broader landscape of big data because of its fundamental role in a productive, thriving society. Building on automation, artificial intelligence (AI) has the potential to revolutionize healthcare and help address some of the challenges set out above. The application of AI to healthcare data can literally be a matter of life and death. AI can assist doctors, nurses, and other healthcare workers in their daily work. AI in healthcare can enhance preventive care and quality of life, produce more accurate diagnoses and treatment plans, and lead to better patient outcomes overall. This book gives insights into the latest developments of applications of AI in biomedicine, including disease diagnostics, pharmaceutical processing, patient care and monitoring, biomedical information, and biomedical research. It also presents an outline of the recent breakthroughs in the application of AI in healthcare, describes a roadmap to building effective, reliable, and safe AI systems, and discusses the possible future direction of AI augmented healthcare systems. AI has countless applications in healthcare. Whether it’s being used to discover links between genetic codes, to power surgical robots or even to maximize hospital efficiency; AI has been a boon to the healthcare industry.

Health-Care Utilization as a Proxy in Disability Determination

Health-Care Utilization as a Proxy in Disability Determination
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
Total Pages: 161
Release: 2018-04-02
Genre: Medical
ISBN: 030946921X
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The Social Security Administration (SSA) administers two programs that provide benefits based on disability: the Social Security Disability Insurance (SSDI) program and the Supplemental Security Income (SSI) program. This report analyzes health care utilizations as they relate to impairment severity and SSA's definition of disability. Health Care Utilization as a Proxy in Disability Determination identifies types of utilizations that might be good proxies for "listing-level" severity; that is, what represents an impairment, or combination of impairments, that are severe enough to prevent a person from doing any gainful activity, regardless of age, education, or work experience.

Artificial Intelligence in Healthcare

Artificial Intelligence in Healthcare
Author: Adam Bohr
Publisher: Academic Press
Total Pages: 385
Release: 2020-06-21
Genre: Computers
ISBN: 0128184396
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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

AI Implementation in Radiology

AI Implementation in Radiology
Author: Erik Ranschaert
Publisher: Springer
Total Pages: 0
Release: 2024-10-30
Genre: Medical
ISBN: 9783031689413
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This book describes change management in the context of implementing AI in medicine and radiology. Why do many medical institutions struggle to use AI in their clinical practice? What are the essential steps for and before an effective implementation of AI in radiology workflow? How can AI implementation trigger enduring improvements in the clinical process? The book shows how change management is crucial to effectively introduce AI to medicine and radiology, transform healthcare delivery and ensure a smooth transition while maximizing the benefits of AI and minimizing potential disruptions. Change management in the context of AI in medicine and radiology involves a systematic approach to identify, plan, implement, and evaluate the integration of AI technologies into healthcare systems. It engages the necessary stakeholders at the appropriate points in the process to ensure that change is implemented properly. By effectively managing the change, healthcare organizations can harness the potential of AI to enhance patient care, improve diagnosis accuracy, and optimize operational efficiency in radiology and other medical specialties. Throughout this change management process, organizations should prioritize ethical considerations, data privacy, and regulatory compliance to ensure that AI technologies are deployed responsibly and in accordance with relevant guidelines and regulations.

Intelligent Healthcare

Intelligent Healthcare
Author: Surbhi Bhatia
Publisher: Springer Nature
Total Pages: 323
Release: 2021-07-02
Genre: Technology & Engineering
ISBN: 3030670511
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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.

Artificial Intelligence in Clinical Practice

Artificial Intelligence in Clinical Practice
Author: Chayakrit Krittanawong
Publisher: Elsevier
Total Pages: 550
Release: 2023-09-29
Genre: Computers
ISBN: 0443156891
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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. This is a valuable resource for clinicians, students, researchers and members of medical and biomedical fields who are interested in learning more about artificial intelligence technologies and their applications in medicine. Provides the history and overview of the various modalities of AI and their applications within each field of medicine Discusses current AI-based medical research, including landmark trials within each field of medicine Addresses the current knowledge gaps that clinicians commonly face that prevent the application of AI-based research to clinical practice Encompasses examples of specific cases and discusses challenges and biases associated with AI

AI-First Healthcare

AI-First Healthcare
Author: Kerrie L. Holley
Publisher: "O'Reilly Media, Inc."
Total Pages: 222
Release: 2021-04-19
Genre: Business & Economics
ISBN: 1492063126
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AI is poised to transform every aspect of healthcare, including the way we manage personal health, from customer experience and clinical care to healthcare cost reductions. This practical book is one of the first to describe present and future use cases where AI can help solve pernicious healthcare problems. Kerrie Holley and Siupo Becker provide guidance to help informatics and healthcare leadership create AI strategy and implementation plans for healthcare. 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. Cowritten by an AI technologist and a medical doctor who leverages AI to solve healthcare’s most difficult challenges, this book covers: The myths and realities of AI, now and in the future Human-centered AI: what it is and how to make it possible Using various AI technologies to go beyond precision medicine How to deliver patient care using the IoT and ambient computing with AI How AI can help reduce waste in healthcare AI strategy and how to identify high-priority AI application

Artificial Intelligence in Medicine

Artificial Intelligence in Medicine
Author: Joseph JY Sung
Publisher: Elsevier
Total Pages: 162
Release: 2024-03-29
Genre: Science
ISBN: 0323950698
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Although AI is opening new and exciting opportunities in healthcare, implementation still faces challenges. Artificial Intelligence in Medicine: From Ethical, Social, and Legal Perspectives provides answers on how to improve acceptance and diminish the anxiety of the use of AI-assisted medicine. Through a series of social, ethical, and legal discussions from clinicians, social scientists, ethicists, and legal experts this important reference has coverage that includes good data custodianship and stewardship; data access, data bias, data & healthcare equity; privacy and confidentiality; algorithmic understanding; and regulatory guidance, accountability, and legal responsibility. This reference will explain to healthcare providers how AI will enhance healthcare, will introduce to scientists and researchers the ethical and social aspect of AI that needs to be addressed, and will urge policymakers and health authorities to consider the legal framework needed to implement AI technology in healthcare. Discusses the issues that must be addressed to improve acceptance and diminish the anxiety and lack of trust surrounding the care of human health by machines Examines the delicate issues surrounding the use of AI in making life-and-death decisions Sets the framework of social, ethical, and legal aspects of healthcare for the future

Machine Learning and AI for Healthcare

Machine Learning and AI for Healthcare
Author: Arjun Panesar
Publisher: Apress
Total Pages: 407
Release: 2020-12-25
Genre: Computers
ISBN: 9781484265369
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This updated second edition offers a guided tour of machine learning algorithms and architecture design. It provides real-world applications of intelligent systems in healthcare and covers the challenges of managing big data. The book has been updated with the latest research in massive data, machine learning, and AI ethics. It covers new topics in managing the complexities of massive data, and provides examples of complex machine learning models. Updated case studies from global healthcare providers showcase the use of big data and AI in the fight against chronic and novel diseases, including COVID-19. The ethical implications of digital healthcare, analytics, and the future of AI in population health management are explored. You will learn how to create a machine learning model, evaluate its performance, and operationalize its outcomes within your organization. Case studies from leading healthcare providers cover scaling global digital services. Techniques are presented to evaluate the efficacy, suitability, and efficiency of AI machine learning applications through case studies and best practice, including the Internet of Things. You will understand 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. What You Will Learn Understand key machine learning algorithms and their use and implementation within healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Manage the complexities of massive data Be familiar with AI and healthcare best practices, feedback loops, and intelligent agents Who 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.