Perceived Impact of Artificial Intelligence on Work-Related Stress Among Nigerian Radiographers: A Cross-Sectional Survey
DOI:
https://doi.org/10.53797/jthkks.v7i2.4.2026Keywords:
Artificial Intelligence, Work-related stress, Medical imaging, Online survey, Radiography, NigeriaAbstract
The rapid demand for utilisation of medical imaging has led to increased workload among radiographers. The integration of artificial intelligent system into imaging for seamless workflows, equipment operation, medical safety and image review processes has potential to assist radiographers with many of the demanding tasks. However, such advances in Artificial Intelligence (AI) may also introduce a new source of work-related stress, which has yet to be fully addressed. This study assessed Work-Related Stress (WRS), evaluated AI's perceived impact, and examined their relationship among Nigerian radiographers. An exploratory cross-sectional online survey of 296 Nigerian radiographers (June-October 2023) assessed WRS causes, AI perceptions, and job security concerns. Spearman's correlation and multiple regression were used to examine relationships, with Bonferroni correction for multiple comparisons. Reliability was confirmed (Cronbach's α: 0.79-0.86). Most respondents (64.5%) affirmed WRS directly impacts well-being and performance, with intensive work (mean=3.84/5) the primary stressor. Over 84.1% believed AI would assist workloads and improve clinical care, significantly correlated with anticipated stress reduction (ρ=0.614, p<0.001). However, 43% lacked coding skills, and concerns about role extension (mean=2.45/3) and job displacement (2.41/3) persisted. Regression revealed AI perception as the strongest predictor of anticipated stress reduction (β=0.482, p<0.001), with coding skills (β=0.218, p=0.008) as secondary. AI holds significant potential to reduce WRS among Nigerian radiographers, yet successful implementation requires addressing significant skill gaps and job security concerns through targeted upskilling, clear role definition, and supportive management.Downloads
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Copyright (c) 2026 Shakirah G. AbdulAzeez, Kudirat Lawal-Adesina, Musa Y. Dambele, Womi C. Iboh, Abdul Fatai K. Bakre
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