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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Large language model-driven agents in nursing practice: A scoping review.

Xinglin Zheng1, Huina Zou1, Linjing Wu1

  • 1Nursing Department, Xiamen Cardiovascular Hospital, Xiamen University, Xiamen, China.

International Journal of Nursing Sciences
|December 10, 2025
PubMed
Summary

Large language model-driven agents (LLMDAs) in nursing enhance care accuracy and efficiency. Addressing ethical and technical challenges is crucial for their successful clinical implementation and future research.

Keywords:
AgentsEthical challengesLarge language modelMulti-agent collaborationNursing

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Area of Science:

  • Nursing Informatics
  • Artificial Intelligence in Healthcare
  • Digital Health

Background:

  • The integration of advanced AI, specifically large language model-driven agents (LLMDAs), presents a transformative opportunity in nursing practice.
  • Understanding the current landscape of LLMDAs in nursing is essential for guiding future technological advancements and clinical applications.

Purpose of the Study:

  • To systematically analyze the technological frameworks, application scenarios, and outcomes of LLMDAs in nursing.
  • To identify and summarize the ethical, technological, and practical challenges associated with LLMDA implementation in nursing.

Main Methods:

  • A scoping review adhering to JBI guidelines was conducted.
  • Searches were performed across five major databases (PubMed, Embase, Web of Science, APA PsycNet, Cochrane Library) for English-language studies published up to September 9, 2025.
  • Eligible studies underwent title/abstract screening followed by independent full-text assessment by two reviewers.

Main Results:

  • Twenty-five studies (2023-2025) from nine countries were included, with a focus on China and the US.
  • Technological architectures included collaborative, augmentative, and interactive models applied in clinical, home-based, and community care settings.
  • LLMDAs demonstrated potential in improving diagnostic accuracy, resource allocation, and patient experience, while highlighting challenges in data privacy, content reliability, and responsibility attribution.

Conclusions:

  • LLMDAs represent a novel paradigm for intelligent transformation in nursing care.
  • These agents show significant potential for enhancing clinical decision-making, care delivery efficiency, and patient satisfaction.
  • Overcoming existing ethical, technical, and practical hurdles is imperative for widespread clinical adoption.