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Introduction: Despite the rapid increase of AI-enabled applications deployed in clinical practice, many challenges exist around AI implementation, including the clarity of governance frameworks, usability of validation of AI models, and customisation of training for radiographers. This study aimed to explore the perceptions of diagnostic and therapeutic radiographers, with existing theoretical and/or practical knowledge of AI, on issues of relevance to the field, such as AI implementation, including knowledge of AI governance and procurement, perceptions about enablers and challenges and future priorities for AI adoption., Methods: An online survey was designed and distributed to UK-based qualified radiographers who work in medical imaging and/or radiotherapy and have some previous theoretical and/or practical knowledge of working with AI. Participants were recruited through the researchers' professional networks on social media with support from the AI advisory group of the Society and College of Radiographers. Survey questions related to AI training/education, knowledge of AI governance frameworks, data privacy procedures, AI implementation considerations, and priorities for AI adoption. Descriptive statistics were employed to analyse the data, and chi-square tests were used to explore significant relationships between variables., Results: In total, 88 valid responses were received. Most radiographers (56.6 %) had not received any AI-related training. Also, although approximately 63 % of them used an evaluation framework to assess AI models' performance before implementation, many (36.9 %) were still unsure about suitable evaluation methods. Radiographers requested clearer guidance on AI governance, ample time to implement AI in their practice safely, adequate funding, effective leadership, and targeted support from AI champions. AI training, robust governance frameworks, and patient and public involvement were seen as priorities for the successful implementation of AI by radiographers., Conclusion: AI implementation is progressing within radiography, but without customised training, clearer governance, key stakeholder engagement and suitable new roles created, it will be hard to harness its benefits and minimise related risks., Implications for Practice: The results of this study highlight some of the priorities and challenges for radiographers in relation to AI adoption, namely the need for developing robust AI governance frameworks and providing optimal AI training., Competing Interests: Conflict of interest statement Nothing to declare., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

On-treatment imaging in radiotherapy has evolved over the last 60 years, bringing with it changes in the roles of radiographers, radiologists and oncologists. The ability to acquire and interpret high quality images (2D kilovoltage and megavoltage imaging and 3D CT and cone-beam CT) for radiotherapy planning and delivery requires therapy radiographers to have skills and knowledge that overlap with those of diagnostic radiographers. With the implementation of MRI-guided radiotherapy, treatment radiographers and clinical oncologists are exploring new territory, requiring truly collaborative working practices with their radiology partners. This short communication introduces the first images acquired using the hybrid MR Linac at our institution., (Copyright © 2018 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.)