ReviewFrontiers in public health2026
Equity-centred, nurse-led implementation of artificial intelligence in community maternal and child health nursing: a conceptual framework for low-resource settings.
Review in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Substantial yet preventable maternal and neonatal mortality persists across low- and middle-income countries (LMICs), where shortages of skilled maternal and child health (MCH) nurses, inadequate infrastructure and socioeconomic inequalities continue to limit access to quality care. Artificial intelligence (AI), particularly machine-learning-driven decision-support and predictive systems, is increasingly proposed to strengthen MCH services through risk stratification, early diagnosis and clinical decision-making. However, evidence supporting its equitable, contextually appropriate and sustainable implementation in resource-constrained settings remains limited. Existing literature has inadequately addressed the structural, ethical and relational dimensions of AI adoption from a nursing perspective, and there is a lack of frameworks integrating equity, nurse-led governance and contextual adaptation for LMICs. Drawing on a purposive narrative review, this Perspective proposes an equity-centred, nurse-led framework for AI-assisted MCH nursing in low-resource settings. The framework comprises six interdependent domains: contextual algorithm validation, humanised and relational nursing care, community co-design and engagement, digital equity and inclusion, transparent data governance, and longitudinal monitoring and evaluation. Its novelty lies in positioning equity as a prerequisite for deployment, identifying the nurse-patient relationship as the principal mechanism through which AI influences outcomes, and assigning governance authority to nurses and communities. A phased implementation pathway is proposed to guide responsible deployment. The framework remains conceptual and requires prospective empirical validation. Responsible AI integration in MCH nursing demands equity-centred governance, investment in nursing capacity, community participation and locally grounded validation strategies.
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