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"This book discusses how AI technologies can assist physicians to make better clinical decisions enabling early detection of subclinical organ dysfunction, through the use of clinically relevant information that can be found in the massive amount of data and, thus, improving quality and efficiency of healthcare delivery"--
Machine learning approaches have great potential in increasing the accuracy of cardiovascular risk prediction and avoiding unnecessary treatment. The application of machine learning techniques may improve heart failure outcomes and management, including cost savings by improving existing diagnostic and treatment support systems. Additionally, artificial intelligence technologies can assist physicians in making better clinical decisions, enabling early detection of subclinical organ dysfunction, and improving the quality and efficiency of healthcare delivery. Further study on these innovative technologies is required in order to appropriately utilize the technology in healthcare. Leveraging AI Technologies for Preventing and Detecting Sudden Cardiac Arrest and Death provides insight into the causes and symptoms of sudden cardiac death and sudden cardiac arrest while evaluating whether artificial intelligence technologies can improve the accuracy of cardiovascular risk prediction. Furthermore, it consolidates the current open issues and future technology-driven solutions for sudden cardiac death and sudden cardiac arrest prevention and detection. Covering a number of crucial topics such as wearable sensors and smart technologies, this reference work is ideal for diagnosticians, IT specialists, data scientists, healthcare workers, researchers, academicians, scholars, practitioners, instructors, and students.
Instructional technologies used to be optional and supplemental pedagogical tools until the global health crisis of 2020 compelled education systems to rely on digital devices and services to guarantee academic continuity. Suddenly, the contemporary principles and practices utilized in delivering health education curricula were insufficient and ineffective. Acknowledging the vital role of technology in shaping the future of education, there is now a greater demand to foster innovative interventions and continuous improvement in strategies, methodologies, and systems to empower learners, educators, and leaders in the digital age. This paradigm shift requires a fundamental transformation in the way we approach teaching and learning, and a willingness to embrace new approaches and tools that can enhance the quality of education and support student success. The Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines provides comprehensive coverage of innovative methods and strategies to produce the next generation of health professionals. The book lays the groundwork for implementable teaching and learning models that facilitate knowledge acquisition, enhance perceptual variation, improve skill coordination, and develop a scientific and technological mindset. Each chapter provides an in-depth examination of instructional technologies contextualized in various medical and health domains, including nursing, physiotherapy, radiology, neurophysiology, physical health, dentistry, clinical medicine, and more. This reference work is a must-read for all stakeholders in health education and related fields, including educators, students, researchers, administrators, and healthcare professionals.
Multiple-criteria decision making, including multiple rule-based decision making, multiple-objective decision making, and multiple-attribute decision making, is used by clinical decision makers to analyze healthcare issues from various perspectives. In practical healthcare cases, semi-structured and unstructured decision-making issues involve multiple criteria that may conflict with each other. Thus, the use of multiple-criteria decision making is a promising source of practical solutions for such problems. AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management investigates the contributions of practical multiple-criteria decision analysis applications and cases for healthcare management. The book also considers the best practices and tactics for utilizing multiple-criteria decision making to ensure the technology is utilized appropriately. Covering key topics such as fuzzy data, augmented reality, blockchain, and data transmission, this reference work is ideal for computer scientists, healthcare professionals, nurses, policymakers, researchers, scholars, academicians, practitioners, educators, and students.
Artificial intelligence (AI) has emerged as a transformative force across various domains, revolutionizing the way we perceive and address challenges in healthcare. The convergence of AI and healthcare holds immense promise, offering unprecedented opportunities to enhance medical diagnosis, treatment, and patient care. In today’s world, the intersection of AI and healthcare stands as one of the most promising frontiers for innovation and progress. Artificial Intelligence Transformations for Healthcare Applications: Medical Diagnosis, Treatment, and Patient Care embodies this convergence, offering a comprehensive exploration of how AI is revolutionizing various aspects of healthcare delivery. At its core, this book addresses the urgent need for more effective and efficient healthcare solutions in an increasingly complex and data-rich environment. Covering topics such as chronic disease, image classification, and precision medicine, this book is an essential resource for healthcare professionals, medical researchers, AI and machine learning specialists, healthcare administrators and executives, medical educators and students, biomedical engineers, healthcare IT professionals, policy makers and regulators, academicians, and more.
With the advancement of sensorial media, objects, and technologies, multimedia can play a significant role in smart healthcare by offering better insight of heterogeneous healthcare multimedia content to support affordable and quality patient care. While researchers and the scientific community have been making advances in the study of multimedia tools and healthcare services individually, very little attention has been given to developing cost effective and affordable smart healthcare services. Multimedia-based smart healthcare has the potential to revolutionize many aspects of our society; however, many technical challenges must be addressed before this potential can be realized. Using Multimedia Systems, Tools, and Technologies for Smart Healthcare Services includes high-quality research on the recent advances in various aspects of intelligent interactive multimedia technologies in healthcare services and, more specifically, in the state-of-the-art approaches, methodologies, and systems in the design, development, deployment, and innovative use of multimedia systems, tools, and technologies for providing insights into smart healthcare service demands. Covering topics such as genetic algorithms, automatic classification of diseases, and structural equation modeling, this premier reference source is an essential resource for hospital administrators, medical professionals, health IT specialists, hospital technicians, students and faculty of higher education, researchers, and academicians.
Recent advancements in medical technology, such as telehealth services, have influenced the healthcare sector tremendously. While telehealth technology and its application are not new, it has not been widely utilized despite the numerous benefits and opportunities it provides. However, recent policy changes have lowered obstacles to telehealth access and pushed the use of telemedicine to deliver acute, chronic, primary, and specialist care. In order to successfully integrate this technology in all areas of healthcare, further study is required to fully understand the best practices and challenges of adoption. Advancement, Opportunities, and Practices in Telehealth Technology discusses advances in the digital health technology and telemedicine domains as well as key challenges, solutions, and opportunities regarding their use in healthcare. The book also introduces critical communication protocols, interconnections, system designs, and developments that are extensively used in the present-day telehealth process. Covering a wide range of topics such as digital twins, big data analytics, and robotics, this reference work is an ideal resource for engineers, industry professionals, hospital administration, policymakers, researchers, scholars, academicians, practitioners, instructors, and students.
The healthcare industry is predominantly moving towards affordable, accessible, and quality health care. All organizations are striving to build communication compatibility among the wide range of devices that have operated independently. Recent developments in electronic devices have boosted the research in the medical imaging field. It incorporates several medical imaging techniques and achieves an important goal for health improvement all over the world. Despite the significant advances in high-resolution medical instruments, physicians cannot always obtain the full amount of information directly from the equipment outputs, and a large amount of data cannot be easily exploited without a computer. Machine Learning and AI Techniques in Interactive Medical Image Analysis discusses how clinical efficiency can be improved by investigating the different types of intelligent techniques and systems to get more reliable and accurate diagnostic conclusions. This book further introduces segmentation techniques to locate suspicious areas in medical images and increase the segmentation accuracy. Covering topics such as computer-aided detection, intelligent techniques, and machine learning, this premier reference source is a dynamic resource for IT specialists, computer scientists, diagnosticians, imaging specialists, medical professionals, hospital administrators, medical students, medical technicians, librarians, researchers, and academicians.
The internet of medical things provides significant advantages for the well-being of society by increasing the quality of life and reducing medical expenses. An important step towards a smart healthcare system is to utilize the potential of existing technologies in order to deliver the best services to users and improve their circumstances. With the help of internet of medical things technologies, self-care and early diagnosis are influential services in strengthening the healthcare ecosystem, especially those which utilize remote monitoring systems. The Internet of Medical Things (IoMT) and Telemedicine Frameworks and Applications focuses on the role of artificial intelligence, the internet of medical things, and telemedicine as well as the advantages and challenges that can occur from the integration of these technologies. The book also evolves methodologies to develop frameworks for the integration of the internet of medical things and telemedicine. Covering topics such as remote healthcare, medical imaging, and data science, this reference work is ideal for researchers, academicians, scholars, practitioners, instructors, and students.
The medical domain is home to many critical challenges that stand to be overcome with the use of data-driven clinical decision support systems (CDSS), and there is a growing set of examples of automated diagnosis, prognosis, drug design, and testing. However, the current state of AI in medicine has been summarized as “high on promise and relatively low on data and proof.” If such problems can be addressed, a data-driven approach will be very important to the future of CDSSs as it simplifies the knowledge acquisition and maintenance process, a process that is time-consuming and requires considerable human effort. Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems critically reflects on the challenges that data-driven CDSSs must address to become mainstream healthcare systems rather than a small set of exemplars of what might be possible. It further identifies evidence-based, successful data-driven CDSSs. Covering topics such as automated planning, diagnostic systems, and explainable artificial intelligence, this premier reference source is an excellent resource for medical professionals, healthcare administrators, IT managers, pharmacists, students and faculty of higher education, librarians, researchers, and academicians.