Artificial Intelligence in Nursing Care: Strategies for Addressing Implementation Challenges

To identify and synthesize strategies reported in the literature to address challenges associated with implementing artificial intelligence (AI) in nursing care.

AI is increasingly used in healthcare, but its integration into nursing practice remains complex because of organizational, ethical, technological, and professional barriers. This review considers nursing-specific evidence together with broader healthcare evidence judged relevant to nursing implementation.

A structured scoping review methodology was employed using the framework developed by Arksey and O'Malley and enhanced by Levac and colleagues. Established scoping review reporting guidance supported transparency and methodological rigor. Studies were included if they examined AI integration in nursing and described strategies addressing implementation barriers. Data were extracted systematically and analyzed using thematic synthesis.

Peer-reviewed and gray literature were retrieved from major health and technology databases and institutional sources. Eligible publications included empirical, mixed-methods, conceptual, perspective, and opinion papers addressing AI implementation in nursing or broader healthcare contexts with clear relevance to nursing practice.

Six interdependent domains were identified: organizational readiness, digital competency development, ethical governance, participatory design, infrastructure support, and policy alignment. These domains summarize reported and proposed strategies; evidence of effectiveness was limited and heterogeneous.

AI integration in nursing care may be supported by coordinated, ethically grounded, and context-sensitive strategies. The proposed domains require further empirical evaluation across nursing settings.

Organizations may consider actively involving nurses in implementation processes and offering education intended to support digital competence and ethical decision-making; the effects of these approaches should be evaluated.