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Cited by 38Artificial Intelligence in Healthcare and EducationTopic ModelingMachine Learning in Healthcare

Improving Large Language Model Applications in the Medical and Nursing Domains With Retrieval-Augmented Generation: Scoping Review

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Miao, Yiqun, Zhao, Yuhan, Luo, Yuan, Wang, Huiying, Wu, Ying·2025·Journal of Medical Internet Research
DOI 10.2196/80557PMC PMC12587015

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Abstract

Background: Retrieval-augmented generation (RAG) is increasingly used to improve large language models in the medical and nursing domains. However, a comprehensive understanding of its specific architecture and applications in medical and nursing reasoning remains limited. Objective: We aimed to summarize the current state, existing limitations, and future development directions of RAG in the medical and nursing domains. Methods: The PubMed, Web of Science, IEEE Xplore, and arXiv databases were searched for relevant articles using queries that combined terms related to RAG, medical, and nursing domains, covering the period from November 1, 2022, to May 31, 2025. This review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Results: A total of 917 articles were retrieved, of which 67 met the inclusion criteria. Most studies focused on the medical domain (63/67, 94%), while only a few addressed nursing applications (4/67, 6%). The RAG frameworks included in this review were categorized into 5 functional types: text-based RAG (36/67, 54%), knowledge graph–enhanced RAG (17/67, 25%), agentic RAG (6/67, 9%), multimodal RAG (2/67, 3%), and plug-and-play RAG (6/67, 9%). On the basis of the Simon decision-making process theory, we divided the RAG workflow into 4 stages: intent recognition, knowledge retrieval, knowledge integration, and generation. Only 26 studies included explicit reasoning support, and few were aligned with real-world clinical workflows. Only 12 studies attempted to address ethical considerations related to RAG. Conclusions: We identified 4 key shifts in recent RAG development: shifting from surface-level matching toward contextualized intent recognition, from vague semantics toward logic-driven dynamic retrieval, from passive toward active knowledge retrieval, and from simple aggregation toward coherent context construction. However, most RAG systems in the medical and nursing domains have not yet introduced reasoning methods, and those that have are still predominantly reliant on data‑driven associations without causal modeling. This highlights the need to integrate causal mechanisms for more effective and domain-relevant reasoning in health care. Trial Registration: OSF Registries 10.17605/OSF.IO/WBSV5; https://osf.io/wbsv5

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  • The systematic review followed PRISMA reporting guidance and applied a pre-specified review protocol defining the research questions, eligibility criteria, screening steps, and synthesis approach.

    “This systematic review followed PRISMA reporting guidance and applied a pre-specified review protocol defining the research questions, eligibility criteria, screening steps, and synthesis approach [14].”

    From Multi-Agent Retrieval Augmented Generation for Clinical Decision Support: A Systematic Review and Integrative Conceptual Framework (2026) · cites this paper as [14]

    The Methods > Overview section says this scoping review used PRISMA-ScR reporting guidance and was registered with the Open Science Framework. The Methods sections identify research questions, eligibility criteria, screening steps, and a synthesis framework, but our copy does not establish that a pre-specified protocol defined all of them. The source also describes its work as a scoping review, rather than a systematic review. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Multi-agent frameworks were not explicitly employed in [7], but the demonstrated strengths of faithfulness, grounding and clarity lay the groundwork for future agentic extensions supporting additional interpretability.

    “Multi-agent frameworks were not explicitly employed in [7], however the well-demonstrated strengths faithfulness, grounding and clarity lay the groundwork for future agentic extensions which support additional interpretability [14].”

    From Multi-Agent Retrieval Augmented Generation for Clinical Decision Support: A Systematic Review and Integrative Conceptual Framework (2026) · cites this paper as [14]

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Cited by

Sorted by citations

Authors
Miao, Y., Zhao, Y., Luo, Y., Wang, H., & Wu, Y.
Affiliations
Capital Medical University (CN)
Corresponding author
Wu, Ying
ORCID
Venue
Journal of Medical Internet Research
Publisher
JMIR Publications Inc.
Published
2025-10-21
Volume
27
Pages
e80557
ISSN
1438-8871 (ISSN-L)
DOI
10.2196/80557
PMC
PMC12587015
OpenAlex
W4415400381
Cited by
38
References
9
Citations by year
2026: 37 · 2025: 1
FWCI
2.25
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Medical Internet Research (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
National Natural Science Foundation of China (72034005)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
retrieval-augmented generation, large language models, medical reasoning, nursing, knowledge graphs, agentic RAG, clinical decision support, causal reasoning, ethical considerations, knowledge retrieval, clinical workflow, artificial intelligence in healthcare, knowledge integration, context construction, intent classification, knowledge production, knowledge assessment
MeSH terms
Large Language Models, Humans, Language, Nursing, Information Storage and Retrieval

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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