Erik R. Ranschaert – författare

This volume provides the essential topics necessary for understanding the sustainability of AI in healthcare while ensuring the content is educational and informative for residents in training and professionals in radiology/healthcare and in other related fields. The volume sets the foundations of the topic describing sustainability, current technology and its environmental impact, energy needs in healthcare/radiology and associated energy expenditure while presenting the unique challenges related to the current uses of AI in hospital systems. Finally, it proposes measures for a sustainable future for the establishment of AI in healthcare.

This volume provides the essential topics necessary for understanding the sustainability of AI in healthcare while ensuring the content is educational and informative for residents in training and professionals in radiology/healthcare and in other related fields. The volume sets the foundations of the topic describing sustainability, current technology and its environmental impact, energy needs in healthcare/radiology and associated energy expenditure while presenting the unique challenges related to the current uses of AI in hospital systems. Finally, it proposes measures for a sustainable future for the establishment of AI in healthcare.

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 implicationsfor 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.