Modern Radiography Education: Clinical Competence, Simulation, and AI
Mark Alipio
Received: 16 September 2025; Revised: 29 October 2025; Accepted: 28 November 2025; Published: 16 December 2025
DOI: https://doi.org/10.65931/w9z4k2r7
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.
Keywords: assessment, artificial intelligence, clinical learning environment, patient safety, radiography education, simulation
Author Information: Iligan Medical Center College, Iligan, Philippines; [email protected]
Volume 1, Issue 1, December 2025