Applications, Technical Characteristics, and Evaluation Approaches of Retrieval-Augmented Generation in Nursing: Scoping Review
- 1School of Nursing, Henan Medical University, 601 Jinsui Avenue, Hongqi District, Xinxiang, China
- 2Phase I Clinical Research Center, The First Affiliated Hospital of Henan Medical University, Xinxiang, China
- 3Department of Nursing, The First Affiliated Hospital of Henan Medical University, Xinxiang, China
Abstract
Background: Large language models (LLMs) show great potential for widespread application in the field of nursing; however, their limitations include hallucinations, insufficient domain knowledge, and delays in updating evidence. Retrieval-augmented generation (RAG), by combining external knowledge retrieval with text generation, holds promise for optimizing model outputs. However, current evidence on the use of RAG in nursing remains fragmented and has not been systematically synthesized.
Objective: This scoping review aims to map the applications of RAG in the nursing field, identify current research characteristics and gaps, and provide a foundational evidence base to inform future research and development of intelligent tools for nursing practice.
Methods: This study adopted the Arksey and O’Malley scoping review framework and followed PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting standards. We systematically searched 9 databases—PubMed, Embase, Web of Science, Cochrane Library, Scopus, CNKI, VIP Database, Wanfang Database, and Sinomed—from inception to September 2026. Backward and forward citation searches were also conducted to identify additional eligible studies. Eligible publications were restricted to those in English or Chinese during study selection. Two independent reviewers completed study screening and data extraction, and descriptive synthesis was performed for all eligible records.
Results: A total of 28 studies published between 2024 and 2026 were included. The application of RAG in nursing primarily focused on 4 scenarios: patient education (n=9), nursing informatics (n=8), clinical decision support (n=7), and nursing education (n=4). The base LLMs included the GPT series, LLaMA, ChatGLM, and Doubao, among others, while the knowledge bases primarily integrated literature, clinical guidelines, institutional resources, and clinical data. Most studies relied on dense retrieval, while some incorporated hybrid retrieval, knowledge graphs, and reranking to achieve optimization. Generation and task-output evaluations were reported in 27 studies, whereas retrieval evaluation, safety and reliability evaluations, and implementation or outcome evaluations were less frequently reported. Only 4 studies were conducted in real-world clinical settings. Overall, RAG demonstrated potential to improve the accuracy and reliability of LLM-generated outputs in specific nursing tasks; however, most studies validated only in simulated environments.
Conclusions: This review systematically maps and synthesizes the application scenarios, technical characteristics, reporting completeness, evaluation methods, and research outcomes of RAG from the perspective of nursing tasks, further refining the evidence landscape for RAG research in nursing contexts. By summarizing the selection of base LLMs, knowledge bases and retrieval processes, the key elements of technical reports, and the areas for tiered evaluation, this review provides evidence-informed considerations for the design, validation, and reporting of future RAG-based nursing AI systems. The findings may assist clinical nurses, nursing educators, and hospital IT teams in identifying application scenarios, key safety requirements, and evaluation priorities, while also highlighting the necessity of conducting prospective validation within real-world nursing workflows.
Related
- Retrieval-augmented generation in medicine: A scoping review of technical implementations, clinical applications, and ethical considerations
- Explainable AI in Healthcare: A Holistic View of Technical Approaches, Regulatory Frameworks, Reliable Clinical Implementation Insights and Limitations.
- Anchored Drill-Assisted Localization for Percutaneous Endoscopic Cervical Keyhole Surgery in the Treatment of Cervical Spondylotic Radiculopathy: A Technical Report and Preliminary Efficacy Evaluation
- French validation of the short version of the ASA questionnaire: An exploration of the socio-technical imaginaries and cosmology of Socially Interactive Agent evaluation
- Artificial Intelligence in Thoracic Surgical Oncology: Technical Foundations and Applications in Lung and Esophageal Cancer Surgery
BibTeX
@article{chen2026applications,
title = {{Applications, Technical Characteristics, and Evaluation Approaches of Retrieval-Augmented Generation in Nursing: Scoping Review}},
author = {Mengqi Chen and Anna Ma and Chengyun Wang and Chuanwu Lyu and Yonghui He and Zhongkang Xu and Xueyun Li and Danyang Cheng and Jie Li and Lina Wang},
journal = {Journal of Medical Internet Research},
year = {2026},
month = nov,
doi = {10.2196/98495},
url = {https://doi.org/10.2196/98495},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Applications, Technical Characteristics, and Evaluation Approaches of Retrieval-Augmented Generation in Nursing: Scoping Review
AU - Mengqi Chen
AU - Anna Ma
AU - Chengyun Wang
AU - Chuanwu Lyu
AU - Yonghui He
AU - Zhongkang Xu
AU - Xueyun Li
AU - Danyang Cheng
AU - Jie Li
AU - Lina Wang
PY - 2026
DA - 2026/11/05
JO - Journal of Medical Internet Research
DO - 10.2196/98495
UR - https://doi.org/10.2196/98495
AB - Background: Large language models (LLMs) show great potential for widespread application in the field of nursing; however, their limitations include hallucinations, insufficient domain knowledge, and delays in updating evidence. Retrieval-augmented generation (RAG), by combining external knowledge retrieval with text generation, holds promise for optimizing model outputs. However, current evidence on the use of RAG in nursing remains fragmented and has not been systematically synthesized. Objective: This scoping review aims to map the applications of RAG in the nursing field, identify current research characteristics and gaps, and provide a foundational evidence base to inform future research and development of intelligent tools for nursing practice. Methods: This study adopted the Arksey and O’Malley scoping review framework and followed PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting standards. We systematically searched 9 databases—PubMed, Embase, Web of Science, Cochrane Library, Scopus, CNKI, VIP Database, Wanfang Database, and Sinomed—from inception to September 2026. Backward and forward citation searches were also conducted to identify additional eligible studies. Eligible publications were restricted to those in English or Chinese during study selection. Two independent reviewers completed study screening and data extraction, and descriptive synthesis was performed for all eligible records. Results: A total of 28 studies published between 2024 and 2026 were included. The application of RAG in nursing primarily focused on 4 scenarios: patient education (n=9), nursing informatics (n=8), clinical decision support (n=7), and nursing education (n=4). The base LLMs included the GPT series, LLaMA, ChatGLM, and Doubao, among others, while the knowledge bases primarily integrated literature, clinical guidelines, institutional resources, and clinical data. Most studies relied on dense retrieval, while some incorporated hybrid retrieval, knowledge graphs, and reranking to achieve optimization. Generation and task-output evaluations were reported in 27 studies, whereas retrieval evaluation, safety and reliability evaluations, and implementation or outcome evaluations were less frequently reported. Only 4 studies were conducted in real-world clinical settings. Overall, RAG demonstrated potential to improve the accuracy and reliability of LLM-generated outputs in specific nursing tasks; however, most studies validated only in simulated environments. Conclusions: This review systematically maps and synthesizes the application scenarios, technical characteristics, reporting completeness, evaluation methods, and research outcomes of RAG from the perspective of nursing tasks, further refining the evidence landscape for RAG research in nursing contexts. By summarizing the selection of base LLMs, knowledge bases and retrieval processes, the key elements of technical reports, and the areas for tiered evaluation, this review provides evidence-informed considerations for the design, validation, and reporting of future RAG-based nursing AI systems. The findings may assist clinical nurses, nursing educators, and hospital IT teams in identifying application scenarios, key safety requirements, and evaluation priorities, while also highlighting the necessity of conducting prospective validation within real-world nursing workflows.
SN - 1439-4456
SN - 1438-8871
ER -