Artificial Intelligence (AI) in Home-Care and Community Nursing: A Scoping Review

Abstract

Background/Objectives: Artificial intelligence (AI) is increasingly being applied across healthcare, but empirical evidence on its use in home-care and community nursing remains fragmented across technologies, clinical contexts, nursing roles, and stages of implementation. This scoping review aimed to map current AI applications in home-care and community nursing and synthesize reported implementation experiences, benefits, challenges, and future considerations. Methods: Guided by Joanna Briggs Institute methodology and reported according to PRISMA-ScR, eight databases and grey literature via ProQuest were searched for English-language empirical studies published from 1 January 2020 to 14 May 2026. Retrieved records were screened independently by two reviewers, with discrepancies resolved through discussion or consultation with a third reviewer. Data were charted using a pre-designed and pilot-tested extraction form and synthesized descriptively and thematically. Results: The searches identified 3254 records; after duplicate removal, 1085 records were screened, 38 full-text reports were assessed for eligibility, and 16 studies were included. AI applications were grouped into diagnostic and clinical assessment support, remote monitoring and risk prediction, and nursing workflow optimization and care coordination. Most evidence remained early-stage, heterogeneous, and context-specific, with many studies focused on development or validation rather than routine real-world implementation. Reported benefits included support for clinical assessment, proactive monitoring, chronic disease follow-up, and perceived workload reduction. Key challenges included limited external validation, small or context-specific datasets, interoperability barriers, privacy and accountability concerns, algorithmic bias, digital literacy needs, and potential risks to relational and person-centered nursing care. Conclusions: AI may support home-care and community nursing by strengthening assessment, monitoring, and coordination; however, current evidence should be interpreted as emerging rather than definitive because most included studies were early-stage, heterogeneous, and context-specific; therefore, the findings do not support broad claims of routine clinical effectiveness. Future research should prioritize equity-focused and large-scale real-world validation, implementation frameworks, interoperability, transparent governance, ethical safeguards, and nursing workforce preparation before widespread integration into community practice.