ChatGPT and Large Language Models in Contemporary Nursing

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

OBJECTIVES: This narrative review synthesizes published evidence on the applications, benefits, limitations and governance considerations of ChatGPT and large language models (LLMs) in nursing, across three domains: education, clinical practice and workflow management. DESIGN: The article was conducted as a narrative review. METHODS: A structured literature search was conducted in Medline (via PubMed), Scopus and arXiv, covering publications from January 2019 to March 2026. Peer-reviewed original studies, systematic reviews, scoping reviews, narrative reviews and expert commentaries addressing LLM applications in nursing education, clinical practice or workflow were eligible for inclusion. Studies limited exclusively to non-nursing medical specialties without transferable nursing implications were excluded. Findings were narratively synthesized across five thematic domains by authors with subject-matter expertise in each area. RESULTS: In nursing education, ChatGPT demonstrates utility as an adaptive cognitive scaffold, supporting theoretical learning, simulation-based training and virtual patient encounters, though unregulated use poses risks to academic integrity and independent clinical reasoning. In clinical practice, LLMs can assist with preliminary symptom assessment and patient education material generation; however, performance deteriorates markedly in complex or data-sparse clinical scenarios and hallucination rates remain clinically significant. In workflow management, ChatGPT shows promise in reducing documentation burden and supporting administrative tasks, though data privacy obligations under frameworks such as GDPR constrain real-world deployment. Across all domains, concerns persist regarding algorithmic bias, professional accountability and the absence of clear medico-legal frameworks governing AI-related clinical errors. CONCLUSION: ChatGPT and related LLMs are best positioned as auxiliary tools that augment rather than replace professional nursing judgement. Safe and ethical integration requires the development of AI literacy curricula, institutionally governed deployment frameworks, mandatory human-in-the-loop verification protocols and longitudinal evaluation of patient safety outcomes. Nurses must play an active role in shaping the responsible adoption of generative AI in healthcare. IMPLICATIONS FOR PRACTICE AND RESEARCH: 1. Nurses must treat AI-generated content as a preliminary draft requiring mandatory human verification before clinical or documentation use. 2. Institutions should prioritize closed-loop, enterprise-grade AI deployments over public platforms to ensure GDPR compliance. 3. AI literacy must be embedded in undergraduate and continuing nursing education curricula. 4. Longitudinal research on patient safety outcomes following real-world LLM deployment in nursing is urgently needed. REPORTING METHOD: As a narrative review, this article followed established guidance for the conduct and reporting of narrative reviews. PATIENT OR PUBLIC CONTRIBUTION: There was no patient or public involvement in this narrative review.

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