This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.

The book "Digital Transformation in Healthcare 5.0: Metaverse, Nanorobots, and Machine Learning" is a comprehensive discussion of disruptive technologies and their applications in healthcare. The book starts with an overview of blockchain technology's impact on the healthcare sector, emphasizing its potential to improve data security and interoperability. The book also discusses the Metaverse's role in healthcare transformation, utilizing a blockchain method to improve patient care and medical practices. The book also focuses on the interrelationships of Blockchain-Enabled Metaverse Healthcare Systems and Applications, highlighting innovative strategies. It also introduces an Intraocular Pressure Monitoring System for Glaucoma Patients, demonstrating the integration of IoT and Machine Learning for improved care. The book winds up with a Machine Learning Approach to Voice Analysis in Parkinson's disease Diagnosis, demonstrating the potential of voice analysis as a non-invasive diagnostic tool.

Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working

While many effective interventions have been developed with the potential to significantly reduce morbidity and mortality from cancer, they are of no benefit to the health of populations if they cannot be delivered. In response to this challenge, Advancing the Science of Implementation across the Cancer Continuum provides an overview of research that can improve the delivery of evidence-based interventions in cancer prevention, early detection, treatment, and survivorship. Chapters explore the field of implementation science and its application to practice, a broad synthesis of relevant research and case studies illustrating each cancer-focused topic area, and emerging issues at the intersection of research and practice in cancer. Both comprehensive and accessible, this book is an ideal resource for researchers, clinical and public health practitioners, medical and public health students, and health policymakers.

Full four-color book. Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.

This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are strongly related and mutually complementary; accordingly, the new series encourages an integrated approach to themes and topics in these disciplines, which will result in significant cross-fertilization, research advances and new knowledge creation. To maximize the dissemination of research findings, the series will publish edited books, monographs, handbooks, textbooks and conference proceedings. This book is intended for professors, researchers, scientists, engineers and students. An extensive list of references at the end of each chapter allows readers to probe further into those application areas that interest them most.