Home⇛International Journal of Transformative Multidisciplinary Studies⇛vol. 1 no. 1 (2025)

Modern Radiography Education: Clinical Competence, Simulation, and AI

Mark Alipio

Discipline: Education

 

Abstract:

Radiography education faces a clear pressure point. Clinical departments expect graduates who deliver safe examinations, justify imaging requests, optimise protocols, communicate with patients, and work with fast expanding digital systems. Placement capacity, supervision time, and assessment consistency still vary across sites. This review synthesises themes in clinical formation, competence assessment, technology enhanced learning, and curriculum renewal for artificial intelligence and digital professionalism. Evidence shows that clinical learning quality depends on supervision culture, structured feedback, and fair access to learning opportunities, not only on placement hours. Many assessment systems reward task completion more than judgement, patient safety, and adaptability. Simulation and virtual reality widen practice opportunities and protect patients, yet programme impact depends on design quality and alignment with clinical expectations. Digital transformation since the pandemic shapes curriculum delivery, assessment design, and student support. Artificial intelligence education has become a core requirement, yet outcome evidence remains limited. Many studies report confidence or perceived readiness, while fewer link educational change to clinical performance, patient safety, or early career transition.



References:

  1. Alipio, M. M. (2024). Coexist or resist? Impact of artificial intelligence on radiologic technology education. Journal of Medical Imaging and Radiation Sciences, 55(4). https://doi.org/10.1016/j.jmir.2024.101450
  2. Crotty, E., Singh, A., Neligan, N., Chamunyonga, C., & Edwards, C. (2024). Artificial intelligence in medical imaging education: Recommendations for undergraduate curriculum development. Radiography, 30(Suppl. 2), 67–73. https://doi.org/10.1016/j.radi.2024.10.008
  3. Elshami, W., & Abdalla, M. E. (2017). Diagnostic radiography students’ perceptions of formative peer assessment within a radiographic technique module. Radiography, 23(1), 9–13. https://www.sciencedirect.com/science/article/pii/S1078817416300219
  4. Gårdling, A., Viseu, A., Hettinger, A., Jildenstål, P., & Augustinsson, H. (2025). Effects of virtual reality simulation on clinical skills training in undergraduate radiography education: A systematic review. Radiography. https://www.sciencedirect.com/science/article/pii/S1078817425000525
  5. Health and Care Professions Council. (2023). Standards of proficiency: Radiographers.
  6. International Society of Radiographers and Radiological Technologists. (2014). ISRRT radiography education framework. https://www.isrrt.org/education/radiography-education-framework/education-framework-2014/
  7. Khine, R., Harrison, G., & Flinton, D. (2024). What makes a good clinical practice experience in radiography and sonography? An exploration of qualified clinical staff and student perceptions. Radiography, 30(1), 66–72. https://doi.org/10.1016/j.radi.2023.09.013
  8. Lewis, S., Bhyat, F., Casmod, Y., Gani, A., Gumede, L., Hajat, A., Hazell, L., Kammies, C., Mahlaola, T. B., Mokoena, L., & Vermeulen, L. (2024). Medical imaging and radiation science students’ use of artificial intelligence for learning and assessment. Radiography. https://doi.org/10.1016/j.radi.2024.10.006
  9. Miller, G. E. (1990). The assessment of clinical skills/competence/performance. Academic Medicine, 65(9), S63–S67. https://www.ufsj.edu.br/portal2-repositorio/File/napecco/Processos/Miller%201990%20-%20Assesment%20skills%20competence%20performance.pdf
  10. Norcini, J. J. (2003). Work based assessment. BMJ, 326(7392), 753–755. https://pmc.ncbi.nlm.nih.gov/articles/PMC1125657/pdf/753.pdf
  11. O’Connor, M., & McNulty, J. P. (2024). Radiography students’ viewpoints of the clinical learning environment: A cross-sectional study. Radiography, 30(1), 367–374. https://doi.org/10.1016/j.radi.2023.12.005
  12. O’Connor, M., Stowe, J., Potocnik, J., Giannotti, N., Murphy, S., Rainford, L., & McNulty, J. P. (2020). 3D virtual reality simulation in radiography education: The students’ experience. Radiography. https://doi.org/10.1016/j.radi.2020.07.017
  13. Ofori-Manteaw, B., Yeboah, H. S., & Wuni, A.-R. (2024). Enhancing radiography education: The roles and challenges of preceptors in the clinical supervision and training of student radiographers. Radiography, 30(Suppl. 2), 149–155. https://doi.org/10.1016/j.radi.2024.11.020
  14. Santos, J., et al. (2022). Education and training in radiation protection in Europe: Results from the EURAMED rocc-n-roll project. Insights into Imaging, 13, 1–14. https://doi.org/10.1186/s13244-022-01271-y
  15. Sapkaroski, D., Baird, M., McInerney, J., & Dimmock, M. R. (2020). The effectiveness of virtual reality simulation in radiography education: A comparison of two instructional methods for image acquisition and patient positioning. Radiography. https://doi.org/10.1016/j.radi.2020.04.010
  16. Shanahan, M., et al. (2016). Use of a simulated clinical environment in radiography education: Student perspectives. Radiography. https://doi.org/10.1016/j.radi.2016.08.002
  17. Tay, Y. X., & McNulty, J. P. (2023). Radiography education in 2022 and beyond: Writing the history of the present: A narrative review. Radiography, 29(2), 391–397. https://doi.org/10.1016/j.radi.2023.01.014
  18. Tejani, A. S., Elhalawani, H., Moy, L., & Kohli, M. (2022). Artificial intelligence and radiology education. Radiology: Artificial Intelligence, 5(1), e220084. https://doi.org/10.1148/ryai.220084

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