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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.
List of contents
Foreword.- Chapter 1: Introduction - AI-Driven Sustainability in Radiology and Healthcare.- Chapter 2: Introduction to AI and its applications.- Chapter 3: Sustainability in Healthcare from the radiological perspective.- Chapter 4: Environmental footprint of radiology and the role of AI in obtaining sustainable radiology practices.- Chapter 5: Energy-Efficient AI Models and Sustainable Data Management Practices.- Chapter 6: Patients and the Public: Sustainability and AI in Radiology.- Chapter 7: Regulatory issues in sustainable healthcare.- Chapter 8: Towards a Sustainable Future for AI in Healthcare Imaging.- Appendices Glossary of Terms List of Case Studies and Examples.
About the author
Erik Ranschaert
is a radiologist and visiting professor at Ghent University, mainly focusing on artificial intelligence and imaging informatics in radiology. He has worked with several leading institutions, including the Netherlands Cancer Institute, ETZ Tilburg, VU Amsterdam, and the University of Oxford, on developing, validating, and integrating AI-based imaging solutions into clinical practice. From 2018 to 2021, Dr. Ranschaert served as President of the European Society of Medical Imaging Informatics (EuSoMII), where he promoted the adoption of data science, interoperability standards, and AI education within the radiology community. His scientific work includes 29 PubMed-indexed journal articles and 7 book chapters on developing, evaluating, and responsibly implementing AI applications in radiology. Additionally, he actively advises institutions, startups, and companies on AI development and use.
Michail Klontzas
is assistant professor of Radiology at the University of Crete, Greece and group leader of the Artificial Intelligence and Translational Imaging (ATI) Lab at the University of Crete. He holds a PhD from Imperial College London on omics for tissue engineering and a second PhD on artificial intelligence for medical imaging from the University of Crete. He sits in several international committees related to AI including the board of the European Society of Medical Imaging Informatics. He has published over 125 PubMed Indexed papers and several book chapters. His research focuses on the applications of artificial intelligence and radiomics on musculoskeletal and oncological imaging.
Nabile Safdar
, MD, MPH, FSIIM is the Chief AI Officer of Emory Healthcare and an endowed professor and Vice Chair of Informatics in the Department of Radiology and Imaging Sciences at the Emory University School of Medicine. He also holds an appointment in the Department of Health Policy and Management at the Rollins School of Public Health. Dr. Safdar has held multiple leadership roles with the Society of Imaging Informatics in Medicine (SIIM), including service as a board chair. Dr. Safdar is board certified by the American Board of Radiology with a Certificate of Added Qualification in pediatric radiology and is also board certified in clinical informatics by the American Board of Preventive Medicine.
Summary
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.
