PERCEPTIONS OF THE INTEGRATION OF ARTICICIAL INTELLIGENCE INTO THE HEALTH CARE SYSTEM AMONG HEALTH PROFESSIONALS IN NIGERIA
Keywords:
Artificial intelligence in healthcare, Digital health transformation, Healthcare information, Healthcare sustems in NigeriaAbstract
AI is transforming healthcare systems. However, its integration into the healthcare system in Nigeria remains limited. This study investigated perceptions of AI integration, assessing the awareness of AI among healthcare professionals, its influence on health information, and challenges affecting its adoption. Anchored on the Technology Acceptance Model, the study employed a descriptive survey design and engaged 400 healthcare professionals and hospital administrators across health institutions in Nigeria. Data were gathered through structured questionnaires and in-depth interviews and analyzed using descriptive statistics. Findings showed that 83.2% of professionals were familiar with AI. Respondents agreed that AI enhances the accuracy of health information (86.0%), improves medical data interpretation (86.5%), and supports decision-making. Key challenges included insufficient infrastructure (78.9%), high implementation costs (78.9%) and shortage of trained personnel (79.7%). Qualitative findings confirmed that AI improves clinical decision-making and operational efficiency. The study concludes that healthcare professionals in Nigeria recognize the substantial benefits of AI, but its integration remains constrained by infrastructural, financial, and human-capacity challenges. The study recommends sustained investment in digital infrastructure and workforce capacity development for AI-driven healthcare systems in Nigeria.
References
Akhtar, Z. B. (2025). Artificial intelligence within medical diagnostics: A multi-disease perspective. Artificial Intelligence in Health, 2(3), Article 5173. https://doi.org/10.36922/aih.5173
Ali, O., Abdelbaki, W., Shrestha, A., Elbasi, E., Abdallah Ali Alryalat, M., Dwivedie, Y. K., … & Alryalat, D. (2023). A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. Journal of Innovation & Knowledge, 8(1), 100333. https://doi.org/10.1016/j.jik.2023.100333
Al-Nafjan, A., Aljuhani, A., Alshebel, A., Alharbi, A., & Alshehri, A. (2025). Artificial intelligence in predictive healthcare: A systematic review. Journal of Clinical Medicine, 14(19), Article 6752. https://doi.org/10.3390/jcm14196752
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., Al Yami, M. S., Al Harbi, S., & Albekairy, A. M. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC medical education, 23(1), 689. https://doi.org/10.1186/s12909-023-04698-z
Ankolekar, A., Eppings, L., Bottari, F., et al. (2024). Using artificial intelligence and predictive modelling to enable learning healthcare systems for pandemic preparedness. Computational and Structural Biotechnology Journal, 24, 412–419. https://doi.org/10.1016/j.csbj.2024.05.014
Aryee, J. N. A., Davies, P., Torsah, G. A., Apaw, M. M., Boateng, C. D., Mwando, S. M., … Amekudzi, L. K. (2025). Building capacity for artificial intelligence in Africa: A cross-country survey of challenges and governance pathways. Artificial Intelligence for Development, Article 2512.05432. https://doi.org/10.48550/arXiv.2512.05432
Bahadori, S., Buckle, P., Soukup Ascensao, T., Ghafur, S., & Kierkegaard, P. (2025). Evolving digital health technologies: Aligning with and enhancing the evidence standards framework for AI and digital health. JMIR mHealth and uHealth, 13, Article e67435. https://doi.org/10.2196/67435
Barker, D. et al. (2023). Artificial intelligence in healthcare institutions: A systematic literature review on influencing factors. Technology in Society, 76, 102443. https://doi.org/10.1016/j.techsoc.2023.102443
Botha, N. N., Segbedzi, C. E., Dumahasi, V. K., & colleagues. (2024). Artificial intelligence in healthcare: A scoping review of perceived threats to patient rights and safety. Archives of Public Health, 82, 188. https://doi.org/10.1186/s13690-024-01414-1
Chong, P. L., & colleagues. (2025). Integrating artificial intelligence in healthcare: Applications, diagnostic advances, and personalized treatment planning. Journal of Healthcare Informatics and Practice. https://pubmed.ncbi.nlm.nih.gov/40616302/
Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/fhj.2019-0017
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., … Wright, R. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative artificial intelligence for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Federal Ministry of Health. (2021). National digital health strategic framework (2020–2024). Federal Ministry of Health, Nigeria.
Fernandes Prabhu, D. (2025). Integrating artificial intelligence in electronic health records for predictive modelling and diagnostics. Healthcare, 13(21), Article 2753. https://doi.org/10.3390/healthcare13212753
Fetters, M. D., & Tajima, C. (2022). Joint displays of integrated data collection in mixed methods research. International Journal of Qualitative Methods, 21, 1–13. https://doi.org/10.1177/16094069221104564
Hakkim, H. E., et al. (2024). Advances in artificial intelligence and machine learning applications in healthcare. AI, 5(4), Article 95. https://doi.org/10.3390/ai5040095
Huang, J. W. Y., & Lee, M. J. (2025). Technology Acceptance Model in medical education: Systematic review of constructs influencing acceptance and use. JMIR Medical Education, 6, Article e67873. https://doi.org/10.2196/67873
Ibrahim, F., Münscher, J.-C., Daseking, M., & Telle, N.-T. (2025). The technology acceptance model and adopter type analysis in the context of artificial intelligence. Frontiers in Artificial Intelligence, 7, Article 1496518. https://doi.org/10.3389/frai.2024.1496518
Karami, M. (2025). Artificial intelligence and digital health in primary health care: Challenges and policy insights for LMICs. Frontiers in Digital Health, 2, Article 1532361. https://doi.org/10.3389/fdgth.2025.1532361
Kruse, C. S., Stein, A., Thomas, H., & Kaur, H. (2018). The use of electronic health records to support population health: A systematic review of the literature. Journal of Medical Systems, 42(11), 214. https://doi.org/10.1007/s10916-018-1075-6
Mienye, I. D., Sun, Y., & Ileberi, E. (2024). Artificial intelligence and sustainable development in Africa: A comprehensive review. Machine Learning with Applications, 7, Article 100591. https://doi.org/10.1016/j.mlwa.2024.100591
Mohajer-Bastami, A., et al. (2025). Artificial intelligence in healthcare: A narrative review of applications, challenges, and future directions. Frontiers in Digital Health. https://doi.org/10.3389/fdgth.2025.1644041
Nwoke, J. (2024). Healthcare data analytics and predictive modelling: Enhancing outcomes in resource allocation and disease forecasting. International Journal of Health Sciences. https://doi.org/10.47941/ijhs.2245
Okwukwu, M., Olofin, D. O., & Akintayo Taiwo, A. (2025). Artificial intelligence in Nigeria healthcare: A review of state, challenges and opportunities. EthAIca, 4, 210. https://doi.org/10.56294/ai2025210
Olawade, D. B. (2024). Artificial intelligence in healthcare delivery: Prospects, challenges, and ethical considerations. Computer Methods and Programs in Biomedicine Update. https://doi.org/10.1016/j.cmpbup.2024.100158
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature medicine, 28(1), 31–38. https://doi.org/10.1038/s41591-021-01614-0
Topol, E. J. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.
Tshimula, J. M., Kalengayi, M., Makenga, D., Lilonge, D., Asumani, M., Muabila, J. T., … Mpinga, E. K. (2024). Artificial intelligence for public health surveillance in Africa: Applications and opportunities. Preprint. https://arxiv.org/abs/2408.02575
World Health Organization. (2021). Global strategy on digital health 2020–2025. World Health Organization. https://www.who.int/publications/i/item/9789240020924Bottom of Form
Yang, H. J., Lee, J.-H., & Lee, W. (2025). Factors influencing health care technology acceptance in older adults based on TAM and UTAUT: Meta-analysis. Journal of Medical Internet Research, 27(1), e65269. https://doi.org/10.2196/65269