Perceived Impact of Artificial Intelligence on Work-Related Stress Among Nigerian Radiographers: A Cross-Sectional Survey
DOI:
https://doi.org/10.53797/jthkks.v7i2.1.2026Keywords:
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 intelligence systems 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.
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Alyami, A. S., Majrashi, N. A., & Shubayr, N. A. (2023). Radiologists’ and Radiographers’ Perspectives on Artificial Intelligence in Medical Imaging in Saudi Arabia. Current Medical Imaging Formerly Current Medical Imaging Reviews, 20. https://doi.org/10.2174/0115734056250970231117111810
Ashong, G. G. N. A., Rogers, H., Botwe, B. O., & Anim-Sampong, S. (2016). Effects of occupational stress and coping mechanisms adopted by radiographers in Ghana. Radiography, 22(2), 112–117. https://doi.org/10.1016/j.radi.2015.09.002
Bartram, T., Cavanagh, J., Stanton, P., Walker, M., Pariona-Cabrera, P., & Halvorsen, B. (2023). Medical science an undervalued profession: Strengthening professional identity through union strategy. Journal of Industrial Relations, 65(3), 348–369. https://doi.org/10.1177/00221856231168994
Bashar, J. M., Aliyu, D., Anyebe, E. E., Gabriel, I., Nasir, A., Mangari, A. S., Abubakar, F. S., & Wada, Y. H. (2024). Perceived work-related stress and associated factors among the surgical workforce in a Nigerian tertiary health facility: A cross-sectional study. PLOS Global Public Health, 4(11 November). https://doi.org/10.1371/journal.pgph.0003959
Botwe, B. O., Akudjedu, T. N., Antwi, W. K., Rockson, P., Mkoloma, S. S., Balogun, E. O., Elshami, W., Bwambale, J., Barare, C., Mdletshe, S., Yao, B., & Arkoh, S. (2021). The integration of artificial intelligence in medical imaging practice: Perspectives of African radiographers. Radiography, 27(3), 861–866. https://doi.org/10.1016/j.radi.2021.01.008
Botwe, B. O., Antwi, W. K., Arkoh, S., & Akudjedu, T. N. (2021). Radiographers’ perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study. Journal of Medical Radiation Sciences, 68(3), 260–268. https://doi.org/10.1002/jmrs.460
Champendal M, De Labouchère S, Ghotra SS, Gremion I, Sun Z, Torre S, Khine R, Marmy L, Malamateniou C, & Dos Reis CS. (2024). Perspectives of medical imaging professionals about the impact of AI on Swiss radiographers. J Med Imaging Radiat Sci., 55(4). https://doi.org/10.1016/j.jmir.2024.101741
Di Stefano, G., Salerno, S., Matranga, D., Lodico, M., Monzani, D., Seidita, V., Cannella, R., Maniscalco, L., & Miceli, S. (2025). Artificial Intelligence Perceptions and Technostress in Staff Radiologists: The Mediating Role of Artificial Intelligence Acceptance and the Moderating Role of Self-Efficacy. Behavioral Sciences, 15(9). https://doi.org/10.3390/bs15091276
Gale, N. (2025). Are we sleepwalking into a fully automated medical imaging service? Journal of Medical Imaging and Radiation Sciences, 56(5). https://doi.org/10.1016/j.jmir.2025.101969
Hardy, M., & Harvey, H. (2020). Artificial intelligence in diagnostic imaging: impact on the radiography profession 1. Radiography, 26(S1), S48–S53. https://doi.org/10.1016/j.radi.2020.05.008
Jagodič, M., Hlebec, V., & Starc, T. (2020). Identification Of Occupational Stressors Amongst Radiographers. Medical Imaging and Radiotherapy Journal, 37(1). https://doi.org/10.47724/MIRTJ.2020.i01.a004
Katus, L., Foley, S., Murray, A. L., Luong-Thanh, B. Y., Taut, D., Baban, A., Madrid, B., Fernando, A. D., Sikander, S., Ward, C. L., Osafo, J., Marlow, M., Du Toit, S., Walker, S., Van Vo, T., Fearon, P., Valdebenito, S., Eisner, M. P., & Hughes, C. (2022). Perceived stress during the prenatal period: assessing measurement invariance of the Perceived Stress Scale (PSS-10) across cultures and birth parity. Archives of Women’s Mental Health, 25(3), 633–640. https://doi.org/10.1007/s00737-022-01229-5
Kharjahrin, B., & Hrangkhawl, W. (2022). A Study on the Occupational Stress Among the Radiographers Working in Healthcare Centres in Shillong. International Journal of Health Sciences and Research, 12(8), 57–63. https://doi.org/10.52403/ijhsr.20220808
Lakhani, P., Prater, A. B., Hutson, R. K., Andriole, K. P., Dreyer, K. J., Morey, J., Prevedello, L. M., Clark, T. J., Geis, J. R., Itri, J. N., & Hawkins, C. M. (2018). Machine Learning in Radiology: Applications Beyond Image Interpretation. Journal of the American College of Radiology, 15(2), 350–359. https://doi.org/10.1016/j.jacr.2017.09.044
Lewis, S. J., Gandomkar, Z., & Brennan, P. C. (2019). Artificial Intelligence in medical imaging practice: looking to the future. In Journal of Medical Radiation Sciences (Vol. 66, Number 4, pp. 292–295). John Wiley and Sons Ltd. https://doi.org/10.1002/jmrs.369
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017). A Survey on Deep Learning in Medical Image Analysis. https://doi.org/10.1016/j.media.2017.07.005
Liu, H., Ding, N., Li, X., Chen, Y., Sun, H., Huang, Y., Liu, C., Ye, P., Jin, Z., Bao, H., & Xue, H. (2024). Artificial Intelligence and Radiologist Burnout. JAMA Network Open, 7(11). https://doi.org/10.1001/jamanetworkopen.2024.48714
MacCallum, R. C., Widaman, K. F., Preacher, K. J., & Hong, S. (2001). Sample Size in Factor Analysis: The Role of Model Error. Multivariate Behavioral Research, 36(4), 611–637. https://doi.org/10.1207/S15327906MBR3604_06
Michael Promise Ogolodom, Elizabeth Amini Okankwu, Hyacienth Uche Chiegwu, Joan Somto Okeke, Dlama Zira Joseph, Daniel C. Ugwuanyi, Beatrice U. Maduka, Oluwafemi O. Egbeyemi, Clement Ugochukwu Nyenke, & Egop Browson Egop. (2022). Occupational Stress Level and the associated factors among Intern Radiographers in Anambra State, Nigeria. Trop J Med Res, 21(2), 1–9. https://doi.org/10.5281/zenodo.7255639
Ugwu, A. C., Erondu, O. F., & Umeano, U. B. (2011). Psychosocial stress and its predictors among radiographers in south-eastern Nigeria. The South African Radiographer, 49(2). www.sorsa.org.za
Ulonna, N. F., & Adekeye, J. A. (2025). A Comparative Analysis Of National Health Insurance Scheme (Nhis) And National Health Insurance Authority (Nhia) On Universal Health Coverage (Uhc) In Nigeria. Wukari International Studies Journal, 9(4), 252–262. https://doi.org/10.64633/wissj.v9i4.20
Walsh, G., Stogiannos, N., Ohene-Botwe, B., McHugh, K., Spurge, A., Potts, B., Gibson, C., Tam, W., O’Sullivan, C., Quinsten, A. S., Gorga, R. G., Sipos, D., Dybeli, E., Zanardo, M., Sá dos Reis, C., Mekis, N., Buissink, C., England, A., Beardmore, C., … Malamateniou, C. (2025). R-AI-diographers: investigating the perceived impact of artificial intelligence on radiographers’ careers, roles, and professional identity in the UK. Frontiers in Digital Health, 7. https://doi.org/10.3389/fdgth.2025.1603511
World Health Organization. (2021). Ethics and Governance of Artificial Intelligence for Health: WHO Guidance (World Health Organization, Ed.). World Health Organization. who.int
Woznitza, N., International Society of Radiographers, & The European Federation of Radiographer Societies. (2020). Artificial Intelligence and the Radiographer/Radiological Technologist Profession: A joint statement of the International Society of Radiographers and Radiological Technologists and the European Federation of Radiographer Societies. In Radiography (Vol. 26, Number 2, pp. 93–95). W.B. Saunders Ltd. https://doi.org/10.1016/j.radi.2020.03.007
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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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