Imaging Informatics for Healthcare Professionals – serie

This book provides a unique introduction to the vast field of Medical Imaging Informatics for students and physicians by depicting the basics of the different areas in Radiology Informatics. It features short chapters on the different main areas in Medical Imaging Informatics, such as Picture Archiving and Communication Systems (PACS), radiology reporting, data sharing, and de-identification and anonymization, as well as standards like Digital Imaging and Communications in Medicine (DICOM), Integrating the Health Enterprise (IHE) and Health Level 7 (HL7,. Written by experts in the respective fields and endorsed by the European Society of Medical Imaging Informatics (EuSoMII) the scope of the book is based on the Medical Imaging Informatics sub-sections of the European Society of Radiology (ESR) European Training Curriculum Undergraduate Level and Level I.This volume will be an invaluable resource for residents and radiologists and is also specifically suited for undergraduate training.

This book provides practitioners and scientists with insights into diverse aspects of structured reporting to allow them to develop tools and a knowledge base to ensure that this electronic reporting trend is widely applied. After an introduction to reporting in radiology, various parts of structured reporting are discussed in detail, including an overview of standardized reporting systems, standardized reporting language, DICOM structured reporting, template based structured reporting, and modular reporting. The last chapter addresses the interaction of structured reporting with artificial intelligence and its impact on the future of radiology. The last chapter addresses the interaction of structured reporting with artificial intelligence and its impact on the future of radiology.Endorsed by the European Society of Medical Imaging Informatics (EuSoMII), the scope of the book is based on the Medical Imaging Informatics sub-sections of the European Society of Radiology (ESR) European Training Curriculum Level I and II.It is a valuable resource for residents, radiologists and students.

This book aims to provide physicians and scientists with the basics of Artificial Intelligence (AI) with a special focus on medical imaging. The contents of the book provide an introduction to the main topics of artificial intelligence currently applied on medical image analysis. The book starts with a chapter explaining the basic terms used in artificial intelligence for novice readers and embarks on a series of chapters each one of which provides the basics on one AI-related topic. The second chapter presents the programming languages and available automated tools that enable the development of AI applications for medical imaging. The third chapter endeavours to analyse the main traditional machine learning techniques, explaining algorithms such as random forests, support vector machines as well as basic neural networks. The applications of those machines on the analysis of radiomics data is expanded in the fourth chapter to allow the understanding of algorithms used to build classifiers for the diagnosis of disease processes with the use of radiomics. Chapter five provides the basics of natural language processing which has revolutionized the analysis of complex radiological reports and chapter six affords a succinct introduction to convolutional neural networks which have revolutionized medical image analysis enabling automated image-based diagnosis, image enhancement (e.g. denoising), protocolling etc. The penultimate chapter provides an introduction to data preprocessing for use in the aforementioned artificial intelligence applications. The book concludes with a chapter demonstrating AI-based tools already in radiological practice while providing an insight about the foreseeable future.It will be a valuable resource for radiologists, computer scientists and postgraduate students working on medical image analysis.

This book, endorsed by EuSoMII, provides clinicians, researchers and scientists a useful handbook to navigate the intricate landscape of data harmonization, as we embark on a journey to improve the reproducibility, robustness and generalizability of multi-centric real-world data radiomic studies.   In these pages, the authors delve into the foundational principles of radiomics and its far-reaching implications for precision medicine. They describe the different methodologies used in extracting quantitative features from medical images, the building blocks that enable the transformation of images into actionable predictions. This book sweeps from understanding the basis of harmonization to the implementation of all the knowledge acquired to date, with the aim of conveying the importance of harmonizing medical data and providing a useful guidance to enable its applicability and the future use of advanced radiomics-based models in routine clinical practice.  As authors embark on this exploration of data harmonization in radiomics, they hope to ignite discussions, foster new ideas, and inspire researchers, clinicians, and scientists alike to embrace the challenges and opportunities that lie ahead. Together, they elevate radiomics as a reproducible technology and establish it as an indispensable and actionable tool in the quest for improved cancer diagnosis and treatment.

This book describes change management in the context of implementing AI in medicine and radiology. Why do many medical institutions struggle to use AI in their clinical practice? What are the essential steps for and before an effective implementation of AI in radiology workflow? How can AI implementation trigger enduring improvements in the clinical process? The book shows how change management is crucial to effectively introduce AI to medicine and radiology, transform healthcare delivery and ensure a smooth transition while maximizing the benefits of AI and minimizing potential disruptions.Change management in the context of AI in medicine and radiology involves a systematic approach to identify, plan, implement, and evaluate the integration of AI technologies into healthcare systems. It engages the necessary stakeholders at the appropriate points in the process to ensure that change is implemented properly. By effectively managing the change, healthcare organizations can harness the potential of AI to enhance patient care, improve diagnosis accuracy, and optimize operational efficiency in radiology and other medical specialties.Throughout this change management process, organizations should prioritize ethical considerations, data privacy, and regulatory compliance to ensure that AI technologies are deployed responsibly and in accordance with relevant guidelines and regulations.