AI-Enhanced Virtual Simulation as an Adjunct to Skills Laboratory and Clinical Training in Radiography Education: A Commentary from Somalia and Other Resource-Constrained Settin.

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Radiography education requires repeated practice in positioning, exposure selection, image evaluation, communication, and radiation protection. Virtual simulation can provide safe rehearsal before clinical exposure, but virtual reality, conventional digital simulation, and artificial intelligence (AI)-enhanced simulation are distinct technologies and their evidence should not be conflated. This commentary examines how AI-enhanced virtual simulation could complement, rather than replace, physical skills laboratories and supervised clinical training in Somalia and other resource-constrained educational settings. Evidence from radiography education supports virtual simulation for positioning, exposure-factor selection, image appraisal, and preparation for clinical practice, while emerging AI-specific studies suggest potential roles for adaptive case generation, automated feedback, and assessment support. However, AI-generated educational content may be inaccurate, biased, overly recall-focused, or clinically unrealistic; expert validation and psychometric evaluation are therefore required before curricular or high-stakes assessment use. Somalia has emerging undergraduate radiography programs and heterogeneous imaging capacity, but national data describing teaching-laboratory infrastructure, digital radiography vs computed radiography availability, faculty capacity, and clinical placement resources remain limited. Accordingly, country-level scarcity claims should be treated as hypotheses for local needs assessment rather than established facts. We propose a three-layer model: AI-enhanced preparation, focused physical laboratory practice, and supervised clinical placement. The educational goal should be development of capability - the ability to adapt knowledge and skills to uncertain clinical situations - rather than completion of isolated technical competencies alone. This model offers a testable framework for context-sensitive implementation and future evaluation.

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