Tabriz Valiasr International Hospital Publication International Journal of Aging 2980-9827 3 1 2025 01 12 Enhancing Home-Based Nursing Care for Older Adults: A Scoping Review of Artificial Intelligence Applications e9099 e9099 10.34172/ija.9099 EN Bahareh Khodaei https://orcid.org/0009-0008-5463-5083 Mohammad Matin Alishani https://orcid.org/0009-0004-7748-5648 Seyed Mehdi Mirtajaddini Bahram Jafari Sadegh Alizadeh Journal Article 10.34172/ija.9099 2025 10 10 2025 11 24 Objectives: The rapid expansion of artificial intelligence (AI) in healthcare presents new opportunities to enhance home-based nursing care for older adults amid global population aging and the increasing burden of chronic diseases. This review aimed to map the existing evidence on AI applications in home-based nursing care for older adults, identify reported outcomes, and explore implementation challenges and future directions. Design: This scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Setting(s): Studies conducted in home-based nursing, home care, and community care settings for older adults were included. Participants: The review included studies investigating older adults receiving home-based or community-based nursing care and evaluating AI-supported interventions or technologies. Interventions: AI-based interventions including machine learning models, deep learning systems, telemonitoring technologies, clinical decision-support tools, assistive robotics, and virtual health assistants were examined. Outcome Measures: Extracted outcomes included AI application category, reported clinical and operational outcomes, prediction performance, hospital admission reduction, medication adherence, workflow efficiency, patient engagement, and implementation-related challenges. Results: Forty-one studies met the inclusion criteria. AI applications were categorized into ML-based predictive models (n = 16), DL systems (n = 8), AI-enabled telemonitoring (n = 7), clinical decision-support tools (n = 6), and assistive robotics or virtual agents (n = 4). Reported outcomes included fall prediction accuracy (75–92%), reductions in hospital admissions, improved medication adherence, enhanced workflow efficiency, and increased patient engagement. Moreover, recurring challenges encompassed data privacy concerns, algorithm bias, interoperability barriers, user trust issues, and regulatory uncertainties. Conclusions: Overall, AI demonstrates substantial potential to enhance home-based nursing care by supporting early risk detection, personalized care planning, and workflow optimization. However, successful integration requires robust ethical governance, workforce training, transparent evaluation frameworks, and sustained interdisciplinary collaboration to ensure that AI augments, but does not replace, the human-centered foundation of nursing practices.