奇纳
护理部
护理结果分类
护理
术语
护理研究
梅德林
护士教育
团队护理
质量(理念)
医学
初级护理
医疗保健
护理流程
护理管理
心理学
护理评估
护理文献
职业健康护理
知识翻译
健康信息学
护理干预分类
护理实习
作者
Jianwen Zeng,Xule Zhu,Shuying Shen,Sixie Li,Xin Liu,Qiong Zhang,Shihua Cao
摘要
AIM: To examine the overall performance of large language models (LLMs) in generating nursing care plans, clarify their role in nursing practice and identify directions for future research. DESIGN: This study conducted a scoping review in accordance with Arksey and O'Malley's methodological framework. METHODS: Five electronic databases were systematically searched: Web of Science Core Collection, PubMed, Scopus, CINAHL and IEEE Xplore. The search was limited to studies published between 1 June 2018 and 5 April 2026. RESULTS: Fifteen studies were included. Existing studies primarily used nonreal patient cases and evaluated the textual quality of model-generated nursing care plans across a range of specialties. None examined LLM use within real-world clinical nursing workflows. Evaluation criteria mainly focused on accuracy, information quality and reliability, and readability. The strengths of LLMs in nursing care planning were concentrated in text organization, standardized terminology matching, and the initial drafting of nursing goals and interventions. However, important challenges remain, including privacy, hallucination, and bias. CONCLUSIONS: LLMs may serve as assistive tools for generating initial drafts of nursing care plans, but they cannot yet replace nurses' clinical judgement. Future research should further refine evaluation frameworks and examine the impact of LLM-generated nursing care plans within real-world nursing workflows. Nurse-led human-AI collaboration should be emphasized to support the responsible translation of LLM-assisted nursing care planning into practice. IMPACT: This scoping review highlights that, at present, LLMs can only serve as assistive tools in the development of nursing care plans, while nurses remain the primary decision-makers. It also underscores the need to enhance nurses' AI literacy to strengthen human-AI collaboration and facilitate the integration of LLMs as valuable supportive tools in nursing practice. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
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