Artificial Intelligence for Radiographers - Basic Principles, Clinical Applications and Implementation Considerations

This textbook aims to introduce radiographers to the basic principles, ethics, governance and clinical applications of Artificial Intelligence (AI) across different medical imaging modalities including advantages, challenges and future work needed for AI implementation. It is an essential resource for all clinical practitioners, educators, academics, researchers and students, working in medical imaging and radiotherapy, providing a coherent, evidence-based, comprehensive guide to current and future practice.
Understanding both technical and practical aspects of AI through case studies enables safe and effective patient care, state-of-the-art academic radiography education, enhanced interdisciplinary team communication and collaboration, and appreciation of the accountability involved when employing AI models. 
This textbook also offers insights into the impact on careers, future roles and staff and patient acceptability. It also stresses person-centredness as paramount for AI integration into clinical radiography in a chapter co-produced with patients. Furthermore, it offers the perspectives, supportive statements and AI resources of different national and international organisations, professional bodies and learned societies. Moreover, a chapter led by industry experts brings a unique view on requirements for AI innovation and commercialisation, aiming to inspire hopeful innovators and entrepreneurs.
Finally, the textbook discusses the changing role, responsibilities and competencies of radiographers in a future with AI. It highlights the need to update academic curricula, research priorities and policy to reflect the change of clinical practice and prepare the workforce for a digital future.
The editors would like to sincerely thank Dr Charlotte Beardmore, Professor Patrick Brennan, Edward Chan, Samar ElFarra, Dr Kori Stewart, all renowned world leaders in radiography research, education, policy and practice for their kind forewords.
This work is written by the global radiography community as an offering for all radiographers.

List of contents

AI history and basics: From symbolism to neural networks.- 
AI Methods: Understanding AI models, radiomic analysis and performance metrics in medical imaging.- 
Ethics of AI in radiography practice.- AI governance and implementation in radiography practice.- 
AI in projectional radiography.- 
AI in Computed Tomography.- 
AI in MRI.- 
AI in Interventional Imaging and Cardiology.- AI Applications in Ultrasound Imaging.- AI Applications in Nuclear Medicine and Hybrid Imaging.- 
AI in Radiotherapy.- 
Person-centred and personalised care for radiography in the AI era.- Industry perspectives on AI.- 
Radiographer professional bodies contributions.- 
Preparing for a future with AI in radiography.

About the author

Prof. Christina Malamateniou
Christina is a diagnostic radiographer, an Associate Professor and the Director of the CRRa3G research group. She is a world expert on AI in radiography (AI literacy, AI governance, AI leadership and AI impact on the future of professions) and an active researcher over the last 25 years. She has published more than 100 papers with multidisciplinary teams and has a global network of collaborators. She has also developed the first AI module for radiographers, which runs at City St George’s, University of London since 2020. Her lifetime research grant income surpasses £3.5 million. She is also an enthusiastic educator. She has been the chair for the Society and College of Radiographers AI working group (2020-2023), the chair of the EFRS research committee (2023-2025) and the first radiographer member at the Board of the European Society of Medical Imaging Informatics (2023-2025).
Prof. Maryann Hardy
Maryann is a diagnostic radiographer, Professor Emerita at the University of Bradford and Director of Radiant Horizons coaching Ltd. Maryann is passionate about radiographers fulfilling their potential in a digital world and her research includes the position of self in human-computer interaction and influence on behaviour. Maryann has developed Radiography and CT simulation programmes for personalised student learning using machine learning algorithms to guide learning needs. She is widely published and was invited by the European Federation of Radiographer Societies to contribute to a joint position statement on Artificial Intelligence for Radiography.
Prof. Karen Knapp
Karen is a diagnostic radiographer and an academic at the University of Exeter. Karen’s early research focused on osteoporosis and bone health, but this led her to enter the field of AI research. She has worked in AI with collaborators from computing and mathematics and industry partners for approximately 12 years and within these interdisciplinary teams has helped to develop machine learning and deep learning algorithms for Medical Images. Karen is currently the interim lead for health and wellbeing for the Institute of Data Science and Artificial Intelligence (IDSAI) at the University of Exeter, and has previously been chair of the European Federation of Radiographer Societies (EFRS) Research Committee.
Prof. Aarthi Ramlaul
Aarthi is a diagnostic radiographer and an academic at Buckinghamshire New University. Aarthi’s primary research centred on advancing critical thinking within diagnostic radiography education, with a particular focus on how it enhances autonomous clinical decision-making. She maintains a strong interest in the ethico-legal dimensions of professional practice, especially as they intersect with the integration of artificial intelligence in clinical environments. A prolific contributor to the field, Aarthi has edited and authored numerous scholarly works, including five widely used textbooks in medical imaging.

Summary

This textbook aims to introduce radiographers to the basic principles, ethics, governance and clinical applications of Artificial Intelligence (AI) across different medical imaging modalities including advantages, challenges and future work needed for AI implementation. It is an essential resource for all clinical practitioners, educators, academics, researchers and students, working in medical imaging and radiotherapy, providing a coherent, evidence-based, comprehensive guide to current and future practice.
Understanding both technical and practical aspects of AI through case studies enables safe and effective patient care, state-of-the-art academic radiography education, enhanced interdisciplinary team communication and collaboration, and appreciation of the accountability involved when employing AI models. 
This textbook also offers insights into the impact on careers, future roles and staff and patient acceptability. It also stresses person-centredness as paramount for AI integration into clinical radiography in a chapter co-produced with patients. Furthermore, it offers the perspectives, supportive statements and AI resources of different national and international organisations, professional bodies and learned societies. Moreover, a chapter led by industry experts brings a unique view on requirements for AI innovation and commercialisation, aiming to inspire hopeful innovators and entrepreneurs.
Finally, the textbook discusses the changing role, responsibilities and competencies of radiographers in a future with AI. It highlights the need to update academic curricula, research priorities and policy to reflect the change of clinical practice and prepare the workforce for a digital future.
The editors would like to sincerely thank Dr Charlotte Beardmore, Professor Patrick Brennan, Edward Chan, Samar ElFarra, Dr Kori Stewart, all renowned world leaders in radiography research, education, policy and practice for their kind forewords.
This work is written by the global radiography community as an offering for all radiographers.

Product details

• Assisted by: Maryann Hardy (Editor), Karen Knapp (Editor), Karen Knapp et al (Editor), Christina Malamateniou (Editor), Aarthi Ramlaul (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Release: 11.02.2026
• EAN: 9783032050793
• ISBN: 978-3-0-3205079-3
• No. of pages: 334
• Illustrations: IV, 334 p. 118 illus., 98 illus. in color. With online files/update.
• Subjects: Natural sciences, medicine, IT, technology > Medicine > Clinical medicine; Werkstoffprüfung, Data Science, machine learning, Artificial Intelligence, Deep Learning, Radiology, Bildgebende Verfahren, imaging techniques, imaging technology, Radiography, Medical imaging, Governance and ethics, Radiologic technology