Artificial Intelligence for Radiographers

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.

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.