Leveraging Ai Technologies For Preventing And Detecting Sudden Cardiac Arrest And Death
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Leveraging AI Technologies for Preventing and Detecting Sudden Cardiac Arrest and Death
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.
Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines
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.
Artificial Intelligence Transformations for Healthcare Applications: Medical Diagnosis, Treatment, and Patient Care
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.