Governing Artificial Intelligence in Nursing Practice in Low- and Middle-Income Countries: A Critical Integrative Review and Nurse-Centred Sociotechnical Framework
DOI:
https://doi.org/10.65655/OpenChristianPress.2026.118Keywords:
artificial intelligence, low- and middle-income countries, nursing, global health, sociotechnical systems, AI ethics, implementation science, data justice, health equityAbstract
Background: Artificial intelligence (AI) is increasingly promoted as a means of strengthening clinical decision-making, documentation, triage, surveillance, remote monitoring, and health-system efficiency. In low- and middle-income countries (LMICs), where nurses often sustain frontline care amid workforce shortages, fragile infrastructure, limited specialist access, and constrained public resources, AI could support safer and more timely care. Yet its implementation also risks reproducing inequity when tools are imported, poorly validated, culturally mismatched, weakly regulated, or introduced without meaningful nursing participation. Aim: To synthesize current evidence and policy guidance on barriers and enabling conditions for AI implementation in LMIC nursing practice and to develop a nurse-centred sociotechnical governance framework for safe, equitable, and context-responsive adoption. Design: Critical integrative review with framework synthesis. Methods: Peer-reviewed literature, AI reporting standards, implementation science frameworks, nursing workforce evidence, and authoritative global policy documents were synthesized. Sociotechnical systems theory provided the primary analytic lens, supported by value-sensitive design, technology acceptance constructs, implementation outcomes, and prior nurse-centred AI readiness scholarship. Evidence was charted across infrastructure, workforce, data, governance, cultural-linguistic, and workflow domains and interpreted at micro, meso, and macro levels. Results: Six interdependent implementation domains were identified: digital infrastructure and health information systems; nursing AI literacy and professional agency; data quality, representativeness, and sovereignty; ethical, legal, and regulatory governance; cultural-linguistic localization; and human-AI collaboration within nursing workflows. The synthesis shows that AI risk in LMIC nursing is not reducible to technical error. It emerges from interactions among infrastructure instability, constrained labour conditions, imported or poorly validated models, data asymmetries, weak procurement safeguards, regulatory gaps, and the marginalization of nurses from design and oversight. Conclusion: AI implementation in LMIC nursing should be governed as sociotechnical health-system transformation, not technology transfer. Safe adoption requires local validation, nurse participation, data justice, accountable procurement, proportional regulation, cultural-linguistic adaptation, and post-deployment learning governance. Impact: The proposed framework offers policymakers, nursing leaders, educators, regulators, developers, funders, and global health partners a practical structure for integrating AI without weakening nursing judgement, patient dignity, or health equity.
References
American Nurses Association. (2024). The ethical use of artificial intelligence in nursing practice. https://www.nursingworld.org/practice-policy/nursing-excellence/official-position-statements/
Amisha, Malik, P., Pathania, M., & Rathaur, V. K. (2019). Overview of artificial intelligence in medicine. Journal of Family Medicine and Primary Care, 8(7), 2328-2331. https://doi.org/10.4103/jfmpc.jfmpc_440_19
Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthcare Journal, 8(2), e188-e194. https://doi.org/10.7861/fhj.2021-0095
Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. In A. Bohr & K. Memarzadeh (Eds.), Artificial intelligence in healthcare (pp. 25-60). Academic Press. https://doi.org/10.1016/B978-0-12-818438-7.00002-2
Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81, 77-91. http://proceedings.mlr.press/v81/buolamwini18a.html
Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care: Addressing ethical challenges. The New England Journal of Medicine, 378(11), 981-983. https://doi.org/10.1056/NEJMp1714229
Couldry, N., & Mejias, U. A. (2019). The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford University Press. https://doi.org/10.1515/9781503609754
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
D'Ignazio, C., & Klein, L. F. (2020). Data feminism. MIT Press. https://doi.org/10.7551/mitpress/11805.001.0001
Fjeld, J., Achten, N., Hilligoss, H., Nagy, A., & Srikumar, M. (2020). Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for AI. Berkman Klein Center for Internet & Society. https://doi.org/10.2139/ssrn.3518482
Friedman, B., Kahn, P. H., Jr., & Borning, A. (2006). Value sensitive design and information systems. In P. Zhang & D. Galletta (Eds.), Human-computer interaction in management information systems: Foundations (pp. 348-372). M. E. Sharpe.
Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021). The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health, 3(11), e745-e750. https://doi.org/10.1016/S2589-7500(21)00208-9
Greenhalgh, T., Wherton, J., Papoutsi, C., Lynch, J., Hughes, G., A'Court, C., Hinder, S., Fahy, N., Procter, R., & Shaw, S. (2017). Beyond adoption: A new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. Journal of Medical Internet Research, 19(11), e367. https://doi.org/10.2196/jmir.8775
International Monetary Fund. (2023). AI preparedness index. https://www.imf.org/external/datamapper/datasets/AIPI
International Telecommunication Union. (2024). Measuring digital development: Facts and figures 2024. https://www.itu.int/itu-d/reports/statistics/facts-figures-2024/
Kaushik, A., Barcellona, C., Mandyam, N. K., Tan, S. Y., & Tromp, J. (2025). Challenges and opportunities for data sharing related to artificial intelligence tools in health care in low- and middle-income countries: Systematic review and case study from Thailand. Journal of Medical Internet Research, 27, e58338. https://doi.org/10.2196/58338
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17, Article 195. https://doi.org/10.1186/s12916-019-1426-2
Khan, M. S., Umer, H., & Faruqe, F. (2024). Artificial intelligence for low income countries. Humanities and Social Sciences Communications, 11, Article 1422. https://doi.org/10.1057/s41599-024-03947-w
Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., & Denniston, A. K. (2020). Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension. Nature Medicine, 26(9), 1364-1374. https://doi.org/10.1038/s41591-020-1034-x
Mollura, D. J., Culp, M. P., Pollack, E., Battino, G., Scheel, J. R., Mango, V. L., Elahi, A., Schweitzer, A., & Dako, F. (2020). Artificial intelligence in low- and middle-income countries: Innovating global health radiology. Radiology, 297(3), 513-520. https://doi.org/10.1148/radiol.2020201434
Mutabazi, P., & Sangwa, S. (2026). A Nurse-Centered Socio-Technical Readiness Framework for Artificial Intelligence in Clinical Practice. Open Journal of Transformative Education & Lifelong Learning (ISSN: 3105-305X), 2(2). https://doi.org/10.65655/OpenChristianPress.2026.117
Nilsen, P. (2015). Making sense of implementation theories, models and frameworks. Implementation Science, 10, Article 53. https://doi.org/10.1186/s13012-015-0242-0
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
Oxford Insights. (2025). Government AI readiness index 2025. https://oxfordinsights.com/ai-readiness/government-ai-readiness-index-2025/
Panch, T., Mattie, H., & Atun, R. (2019). Artificial intelligence and algorithmic bias: Implications for health systems. Journal of Global Health, 9(2), 020318. https://doi.org/10.7189/jogh.09.020318
Peters, M. D. J., Marnie, C., Tricco, A. C., Pollock, D., Munn, Z., Alexander, L., McInerney, P., Godfrey, C. M., & Khalil, H. (2020). Updated methodological guidance for the conduct of scoping reviews. JBI Evidence Synthesis, 18(10), 2119-2126. https://doi.org/10.11124/JBIES-20-00167
Proctor, E., Silmere, H., Raghavan, R., Hovmand, P., Aarons, G., Bunger, A., Griffey, R., & Hensley, M. (2011). Outcomes for implementation research: Conceptual distinctions, measurement challenges, and research agenda. Administration and Policy in Mental Health and Mental Health Services Research, 38(2), 65-76. https://doi.org/10.1007/s10488-010-0319-7
Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22-28. https://doi.org/10.1177/0141076818815510
Rivera, S. C., Liu, X., Chan, A. W., Denniston, A. K., Calvert, M. J., & SPIRIT-AI and CONSORT-AI Working Group. (2020). Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI extension. Nature Medicine, 26(9), 1351-1363. https://doi.org/10.1038/s41591-020-1037-7
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Sendak, M., Elish, M. C., Gao, M., Futoma, J., Ratliff, W., Nichols, M., Bedoya, A., Balu, S., & O'Brien, C. (2020). The human body is a black box: Supporting clinical decision-making with deep learning. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 99-109. https://doi.org/10.1145/3351095.3372827
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://doi.org/10.1038/s41591-018-0300-7
Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., et al. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467-473. https://doi.org/10.7326/M18-0850
United Nations Educational, Scientific and Cultural Organization. (2021). Recommendation on the ethics of artificial intelligence. https://www.unesco.org/en/legal-affairs/recommendation-ethics-artificial-intelligence
Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B. F., Ibrahim, M., Liu, X., Mateen, B. A., Mathur, P., McCradden, M. D., Morgan, L., Ordish, J., Rogers, C., Saria, S., Ting, D. S. W., ... McCulloch, P. (2022). Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nature Medicine, 28(5), 924-933. https://doi.org/10.1038/s41591-022-01772-9
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
Vyas, D. A., Eisenstein, L. G., & Jones, D. S. (2020). Hidden in plain sight: Reconsidering the use of race correction in clinical algorithms. The New England Journal of Medicine, 383(9), 874-882. https://doi.org/10.1056/NEJMms2004740
World Bank. (2026). Nurses and midwives (per 1,000 people) [Data set]. Retrieved May 14, 2026, from https://data.worldbank.org/indicator/SH.MED.NUMW.P3
World Health Organization. (2021a). Global strategy on digital health 2020-2025. https://www.who.int/publications/i/item/9789240020924
World Health Organization. (2021b). Ethics and governance of artificial intelligence for health: WHO guidance. https://www.who.int/publications/i/item/9789240029200
World Health Organization. (2023). Regulatory considerations on artificial intelligence for health. https://www.who.int/publications/i/item/9789240078871
World Health Organization. (2025a). State of the world's nursing report 2025. https://www.who.int/publications/i/item/9789240110236
World Health Organization. (2025b). Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models. https://www.who.int/publications/i/item/9789240084759
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