Artificial Intelligence in Nursing: Applications, Benefits, Challenges, and Implications — A Scoping Review

Aim To map and synthesize evidence on the applications, benefits, challenges, and implications of artificial intelligence (AI) in nursing practice, education, administration, and research. Design A scoping review guided by the Joanna Briggs Institute (JBI) methodology and reported in line with PRISMA-ScR. The protocol was registered with the Open Science Framework (https://osf.io/ypma2). Methods Seven databases (PubMed, CINAHL, Embase, Scopus, Web of Science, IEEE Xplore, ACM Digital Library) and grey literature sources were searched for English-language studies published between January 2015 and February 2025. Two reviewers independently screened and charted data. Extracted information included study characteristics, AI type, nursing domain, outcomes, barriers, and facilitators. Results Twenty-eight studies were included, spanning 12 countries. Thematic mapping identified four domains: clinical practice (n = 12), education (n = 8), administration (n = 4), and research (n = 4). Benefits included improved patient safety, early detection of deterioration, personalized learning, efficiency in workforce management, and insights from nursing documentation. Barriers involved alarm fatigue, high costs, lack of AI literacy, and concerns about fairness, privacy, and professional autonomy. Facilitators included leadership support, training, integration into existing systems, and demonstrated patient-safety benefits. Conclusion AI in nursing is an expanding field with promising applications but remains limited by ethical, technical, and organizational challenges. Evidence is largely exploratory and outcome-focused evaluations are scarce. Impact Nurses, educators, and leaders must engage in co-design, training, and governance to ensure AI augments rather than undermines nursing values. Policymakers should embed equity, transparency, and accountability into AI integration strategies.