Advancing Healthcare Through Decision Intelligence
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Advancing Healthcare through Decision Intelligence
Advancing Healthcare through Decision Intelligence: Machine Learning, Robotics, and Analytics in Biomedical Informatics demonstrates real-world applications of decision intelligence - specifically machine learning, robotics, and analytics - to drive innovation and improvements in healthcare delivery and outcomes. The book provides a comprehensive overview of the latest developments in decision intelligence technologies, and offers a practical resource that can help navigate through the complex landscape of healthcare decision-making. In 5 themes Advancing Healthcare through Decision Intelligence: Machine Learning, Robotics, and Analytics in Biomedical Informatics focuses on the use of data analytics aad management techniques in healthcare to improve decision-making and healthcare outcomes, on the use of robotics, robots and genomics to improve biomedical informatics, on the use of genomics, clinical decision support systems (CDSS), machine learning, and deep learning to improve precision medicine and personalized treatment and on the ethical implications of using artificial intelligence (AI) in healthcare, ensuring trust in the technology, and making sure that it can be explained and understood by all stakeholders. This book is a valuable resource for health professionals, scientists and researchers, health practitioners, students, and all those who wish to broaden their knowledge in the allied field. - Provide up-to-date knowledge on decision intelligence technologies, such as machine learning, robotics, and data analytics their applications in the field of healthcare - Presents a range of case studies, practical examples, and theoretical concepts that illustrate how decision intelligence can be applied in areas such as diagnosis, treatment planning, and resource allocation - Empowers readers to make informed decisions that can improve the quality of care and outcomes for patients, while also enhancing the efficiency and effectiveness of healthcare systems
Artificial Intelligence in Healthcare
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
Advancing Healthcare through Data-driven Innovations
The book emphasizes the role of data in driving healthcare transformation, providing readers with a roadmap for understanding and effectively implementing data-driven innovations. It delves into the applications of big data analytics, unveiling valuable insights and offering real-time decision support to healthcare professionals and goes on to review the role of machine learning and artificial intelligence in enabling accurate diagnosis, personalized treatment recommendations, and predictive modeling. The book is an invaluable resource for healthcare professionals, researchers, policymakers, and technology enthusiasts alike. Its practical insights and perspectives empower stakeholders to leverage data-driven technologies effectively, thus fostering continuous improvements in patient care and shaping a brighter future for the healthcare industry as a whole.