Applications of Artificial Intelligence Technologies in Nursing for Noncommunicable Chronic Diseases: A Scoping Review.

BACKGROUND: Noncommunicable chronic diseases (NCDs) account for approximately 41 million deaths each year and 74% of global deaths. Artificial intelligence (AI) is increasingly used for risk prediction, monitoring, and decision support in chronic disease care, but its nursing-specific applications, implementation maturity, and ethical reporting remain incompletely mapped. AIM: To map evidence on AI applications in nursing care for NCDs, with attention to application contexts, nursing roles, technical characteristics, care phases, outcomes, and ethical governance. METHODS: A scoping review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). MEDLINE, CINAHL, Web of Science, PsycINFO, IEEE Xplore, ACM Digital Library, and CNKI were searched from 1 January 1985 to 25 June 2025. Grey literature sources and reference lists were also searched. Data were charted using a structured extraction form and synthesised with descriptive statistics and content analysis. RESULTS: Forty-three studies published between 2005 and 2025 were included. Research increased after 2020 (n = 34, 79.07%), and China and the United States each contributed 11 studies (25.58%). Oncology nursing was the most common disease area (n = 17, 39.53%). The most frequently reported AI technologies were natural language processing (n = 14, 32.56%) and risk prediction models (n = 12, 27.91%). AI was mainly used for clinical decision support (n = 17, 39.53%) and risk assessment (n = 14, 32.56%), whereas applications supporting nursing intervention design and outcome evaluation were limited. Ten studies (23.26%) did not report ethics approval. CONCLUSIONS: AI applications in nursing care for NCDs are expanding, but current evidence remains concentrated in assessment-oriented and decision-support functions. Gaps persist in intervention implementation, outcome validation, data representativeness, and ethical oversight. IMPLICATIONS FOR NURSING MANAGEMENT: With AI currently concentrated in hospital-based settings and assessment-oriented functions, and with 32.56% of included studies not reporting nursing or patient outcome validation, nursing leaders should prioritise extending AI implementation and governance to underserved care settings through targeted workforce training, ethical oversight, and systematic evaluation using nursing-sensitive outcomes.