Quality of Nursing Care and Adaptation to Artificial Intelligence in Low‑ and Middle‑Income Countries: A Systematic Review of Empirical Studies

Background Nurses in low- and middle-income countries (LMICs) deliver care under chronic resource constraints, which compromise quality and shape how new technologies, including artificial intelligence (AI), can be adopted. Evidence on how nursing care quality and nurses’ adaptation to AI intersect in LMICs remains fragmented. Methods We conducted a systematic review of empirical and review studies published between 2022 and 2025 that examined (1) the quality of nursing care in LMICs and/or (2) nurses’ perceptions, readiness, and adaptation to AI in clinical practice. Searches of major databases identified ten eligible studies, including systematic and integrative reviews, scoping reviews, cross-sectional surveys, and conceptual frameworks addressing nursing care quality, AI in nursing, or AI implementation in LMIC health systems. Data were synthesized narratively across two domains: nursing care quality and AI-related attitudes and adaptation. Results Studies on care quality reported high levels of missed or delayed nursing care in LMIC acute and critical care settings, driven by chronic understaffing, inadequate skill mix, limited supplies, and weak governance and quality systems; staffing–outcome research in LMICs was sparse and methodologically heterogeneous. AI-focused studies showed nurses were cautiously open to AI’s potential for efficiency, documentation, and decision support, yet concerned about job security, role erosion, liability, data privacy, and loss of human touch. Attitudes and readiness were influenced by emotion regulation, cognitive flexibility, and digital literacy, while implementation in LMICs was constrained by unreliable infrastructure, immature data systems, and limited technical support. Conceptual frameworks proposed baseline AI competencies for all nurses but emphasized phased, context-sensitive implementation and strong governance. Conclusions Nurses in LMICs are attempting to adapt to AI while fundamental deficits in nursing workforce and care environments remain unresolved. Strengthening staffing and basic quality infrastructure, embedding AI literacy in nursing education, involving nurses in digital-health planning, and establishing clear policies on data protection and accountability are essential to ensure that AI augments rather than undermines nursing care quality in LMICs.