A Theoretical Investigation of the Impact of Artificial Intelligence on Radiographer Professional Roles and Workforce Dynamics in Low-Income Country Contexts

The deployment of artificial intelligence in diagnostic imaging is transforming radiographer professional roles in high-income countries, but the theoretical implications of AI adoption for radiographer workforce dynamics, skill requirements, and professional identity in low-income country contexts such as Nigeria are fundamentally different due to contrasting technology deployment pathways, workforce compositions, and healthcare system structures. This study develops an original theoretical investigation of AI's impact on radiographer professional roles and workforce dynamics in low-income country contexts, using Nigeria as the empirical case. The investigation applies a critical realist methodology combining documentary analysis of AI imaging deployment literature from sub-Saharan Africa; Delphi expert consensus with 40 Nigerian radiography stakeholders projecting AI deployment timelines and role impacts; comparative analysis of AI-radiographer interaction patterns in four Nigerian hospitals that have adopted limited AI image analysis tools; and labour economics modelling projecting workforce displacement and augmentation scenarios under alternative AI adoption trajectories. The original theoretical contribution is a Low-Income Country AI-Radiographer Role Transition Model identifying a distinct 'Technology-Bridging Radiographer' role emerging in LMIC AI deployment contexts, characterised by AI tool navigation, output quality supervision, and patient communication augmentation that is structurally different from LMIC-generalised predictions based on high-income country AI displacement literature. Available AI healthcare workforce literature from low-income country contexts identifies the over-application of high-income country AI workforce disruption models as the primary theoretical gap. The Labour Process Theory and Technology-Organisation-Environment Framework provide the analytical reference. Keywords: artificial intelligence, radiographer roles, workforce dynamics, low-income countries, Nigeria.