Computer-aided diagnosis (CAD) in medicine is the result of a large amount of effort expended in the interface of medicine and computer science. As some CAD systems in medicine try to emulate the diagnostic decision-making process of medical experts, they can be considered as expert systems in medicine.
Computer-aided diagnosis (CAD) in medicine is the result of a large amount of effort expended in the interface of medicine and computer science. As some CAD systems in medicine try to emulate the diagnostic decision-making process of medical experts, they can be considered as expert systems in medicine. Furthermore, CAD systems in medicine may process clinical data that can be complex and/or massive in size.
This book provides an introduction to next generation smart screening technology for medical image analysis that combines artificial intelligence (AI) techniques with digital screening to develop innovative methods for detecting breast cancer. The authors begin with a discussion of breast cancer, its characteristics and symptoms, and the importance of early screening.They then provide insight on the role of artificial intelligence in global healthcare, screening methods for breast cancer using mammogram, ultrasound, and thermogram images, and the potential benefits of using AI-based systems for clinical screening to more accurately detect, diagnose, and treat breast cancer. Discusses various existing screening methods for breast cancer Presents deep information on artificial intelligence-based screening methods Discusses cancer treatment based on geographical differences and cultural characteristics
Over the years, the complexity of health systems has grown due to the continuous and constant introduction of new technologies—process, production, and organizational—which have increased the number of stakeholders involved, creating new relationships and new channels through which the various subjects interact. It is necessary to highlight the critical issues and opportunities relating to the innovation of the organization and governance of health services as well as the complementarity of management and leadership. The new health needs require a Copernican revolution in the organization of services: not only offering individual services but also effective permanent care of the patient within institutional and professional assistance networks and effective, efficient, and appropriate pathways. This requires that on an organizational and managerial level, the internal relationships between the branches of the healthcare companies must be reviewed and closer relationships built with the managing bodies of the social and welfare services. The Handbook of Research on Complexities, Management, and Governance in Healthcare proceeds with a reasoned reconstruction of healthcare issues through the problems connected to the complexities, management, and governance in healthcare in light of the recent COVID-19 pandemic. It discusses both the ethical side of health and the economic, organizational, and legal content. Covering topics such as healthcare innovation, taxation for public health, and waste disposal, this major reference work is a comprehensive resource for healthcare administration, directors, executive boards, lawyers, sociologists, government officials and policymakers, students and faculty of higher education, libraries, researchers, and academicians.
This work presents the latest development in the field of computational intelligence to advance Big Data and Cloud Computing concerning applications in medical diagnosis. As forum for academia and professionals it covers state-of-the-art research challenges and issues in the digital information & knowledge management and the concerns along with the solutions adopted in these fields.
Biosignal processing is an important tool in medicine. As such, this book presents a comprehensive overview of novel methods in biosignal theory, biosignal processing algorithms and applications, and biosignal sensors. Chapters examine biosignal processing for glucose detection, tissue engineering, electrocardiogram processing, soft tissue tomography, and much more. The book also discusses applications of artificial intelligence and machine learning for biosignal processing.
Digital Twins for Healthcare: Design, Challenges and Solutions establishes the state-of-art in the specification, design, creation, deployment and exploitation of digital twins' technologies for healthcare and wellbeing. A digital twin is a digital replication of a living or non-living physical entity. When data is transmitted seamlessly, it bridges the physical and virtual worlds, thus allowing the virtual entity to exist simultaneously with the physical entity. A digital twin facilitates the means to understand, monitor, and optimize the functions of the physical entity and provide continuous feedback. It can be used to improve citizens' quality of life and wellbeing in smart cities and the virtualization of industrial processes. Presents the fundamentals of digital twin technology in healthcare Facilitates new approaches for healthcare industry Explores different use cases of digital twins in healthcare
This book lists current and potential biomedical uses of computational intelligence methods. These methods are used in diagnostics and treatment of such diseases as cancer, cardiac diseases, pneumonia, stroke, and COVID-19. Many biomedical problems are difficult; so, often, the current methods are not sufficient, new methods need to be developed. To confidently apply the new methods to critical life-and-death medical situations, it is important to first test these methods on less critical applications. The book describes several such promising new methods that have been tested on problems from agriculture, computer networks, economics and business, pavement engineering, politics, quantum computing, robotics, etc. This book helps practitioners and researchers to learn more about computational intelligence methods and their biomedical applications—and to further develop this important research direction.