The aim of this investigation was to monitor attitudinal changes of staff and students participating in undergraduate professional programmes to the implementation of shared learning over a four-year period. The programmes being studied were the BSc. Occupational Therapy, BSc. Diagnostic Radiography and BSc. Therapeutic Radiography Honours degrees. Each validated programme contained some syllabus areas that were taught together i.e. were shared across the professions. Initially, after a review of the existing literature on this issue, a questionnaire was designed as a research tool to enable both qualitative and quantitative data to be collected and analysed. The quantitative sections of the questionnaire were checked for reliability throughout the four years and achieved positive Cronbach Alpha results ranging from .7083 to .8984 in the four main concepts under investigation, namely the Pitfalls, Benefits, Curriculum Aspects and Social Aspects of the shared programmes. Over the four year period a total of 418 student questionnaires were collected and analysed. In addition to the quantitative data collected, qualitative data were also collected from the questionnaire from extracts of the minutes of Course Committee and Examination Board meetings and from videos of tutorials and seminars. All of these were analysed. The results showed fluctuations in the attitudes of both staff and students to shared learning over the four year period, but all those who participated showed a net favourable change in attitude by the end of the research investigation.

No / This paper will outline the status and basic principles of artificial intelligence (AI) in radiography along with some thoughts and suggestions on what the future might hold. While the authors are not always able to separate the current status from future developments in this field, given the speed of innovation in AI, every effort has been made to give a view to the present with projections to the future. AI is increasingly being integrated within radiography and radiographers will increasingly be working with AI based tools in the future. As new AI tools are developed it is essential that robust validation is undertaken in unseen data, supported by more prospective interdisciplinary research. A framework of stronger, more comprehensive approvals are recommended and the involvement of service users, including practitioners, patients and their carers in the design and implementation of AI tools is essential. Clearer accountability and medicolegal frameworks are required in cases of erroneous results from the use of AI-powered software and hardware. Clearer career pathways and role extension provision for healthcare practitioners, including radiographers, are required along with education in this field where AI will be central. With the current growth rate of AI tools it is expected that many of the applications in medical imaging will continue to develop to more accurate, less expensive and more readily available versions moving from the bench to the bedside. The hope is that, alongside efficiency and increased patient throughput, patient centred care and precision medicine will find their way in, so we will not only deliver a faster, safer, seamless clinical service but also one that will have the patients at its heart. AI is already reaching clinical practice in many forms and its presence will continue to increase over the short and long-term future. Radiographers must learn to work with AI, embracing it and maximising the positive outcomes from this new technology.