Do Large Language Models Support Nursing Care Planning? A Scoping Review of Applications, Evaluation Approaches and...

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.

This study conducted a scoping review in accordance with Arksey and O'Malley's methodological framework.

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.

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.

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.

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.

No patient or public contribution.