Aug 2026· Journal of Clinical Nursing· 0 citations· 81 references
Medicine
TL;DR
ChatGPT and related LLMs are best positioned as auxiliary tools that augment rather than replace professional nursing judgement, and institutions should prioritize closed-loop, enterprise-grade AI deployments over public platforms to ensure GDPR compliance.
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.
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.
Jianwen Zeng, Xule Zhu, Shiying Shen et al.· Journal of Clinical Nursing· 0 citations
LLMs hold substantial potential to enhance healthcare teamwork by supporting clinical decisions, streamlining administrative workflows, and improving patient communication, however, ethical, legal, and accountability concerns remain.
Ilse Super, Olya Rezaeian, Onur Asan· International Journal of Med...· 0 citations
Artificial intelligence (AI) is increasingly embedded Gen-Z, with ChatGPT emerging as an educational tool. Its application has expanded rapidly across disciplines, including medical education, where it is widely used. Robust evidence synthesising its effectiveness in undergraduate medical education is still evolving. This study aimed to evaluate the impact of ChatGPT-assisted teaching compared with traditional educational methods in undergraduate medical students.
A systematic review and meta-analysis were conducted in accordance with PRISMA 2020 guidelines. Independent searches were performed across PubMed, Embase, Ovid full-text journals, and the Cochrane Library using the terms “ChatGPT” and “medical education” in titles and abstracts until 25th September 2025. Randomised controlled trials involving undergraduate medical students were included. The intervention was ChatGPT-assisted teaching, with traditional classroom and bedside teaching as the comparator. Statistical analysis was performed using RevMan 5.0 with a random-effects model.
Six randomised controlled trials met the inclusion criteria, comprising 361 undergraduate medical students (182 in the ChatGPT group and 179 in the traditional teaching group). ChatGPT-assisted learning was associated with significantly higher assessment scores, with a pooled mean difference of 6.21 points in favour of ChatGPT (95% CI 2.03–10.39, p = 0.004). Heterogeneity was substantial (I² = 98%), indicating marked variability between studies.
ChatGPT-assisted teaching significantly improves learning outcomes in undergraduate medical students compared with traditional teaching. However, the very high heterogeneity and potential publication bias necessitate cautious interpretation, and further high-quality trials are required to define optimal educational implementation.
V. Arunagiri, Kothai Anbalagan, Saqib Ali· British Journal of Surgery· 0 citations
Residents serve as frontline educators in contemporary healthcare systems, yet formal preparation for this role remains uncommon globally. The persistent gap between teaching responsibility and structured institutional support represents a significant concern for educational quality, resident professional development, and patient safety. To synthesize the current evidence on residents’ involvement in teaching, their perceptions of the educator role, factors influencing teaching effectiveness, and the outcomes of formal Residents as Teachers (RaT) programs. A structured narrative review was conducted using PubMed, Embase, and ERIC from 2010 to 2025, guided by a PCO (Population, Concept, Outcome) framework. Studies involving medical or surgical residents in postgraduate years 1–6 examining teaching roles, perceptions, or RaT program outcomes were included. Systematic reviews and studies focused on faculty or undergraduate populations were excluded. The screening and reporting process was informed by PRISMA principles; narrative synthesis with thematic organization was applied. Thirty-two studies met inclusion criteria, yielding four interconnected themes: (1) residents universally recognize teaching as central to their professional identity yet carry substantial informal teaching loads without formal preparation; (2) institutional culture, prior experience, and motivational profiles significantly shape teaching effectiveness; (3) residents across specialties and regions consistently request structured training with explicit objectives; and (4) longitudinal, integrated programs produce more durable skill gains than brief stand-alone workshops. Despite substantial informal teaching activity, residents remain underprepared for their educator role. Structured, longitudinal RaT programs are needed, particularly in under-researched healthcare systems, where unique cultural, institutional, and workforce characteristics call for locally developed approaches.
Lateefa Mohamed AlMarzooqi, N. Goswami· BMC Medical Education· 1 citation
Nursing academics appear to adopt AI selectively, prioritising preservation of core professional values while embracing applications perceived to enhance, rather than replace, educational practice, providing evidence for nursing education programs globally regarding faculty development, institutional policy frameworks, and curriculum design strategies integrating technological advancement whilst maintaining person-centred values.
Natasha Hawkins, Anthea Fagan, Yumiko Coffey et al.· Journal of Advanced Nursing· 0 citations
PURPOSE
Multimorbidity is an increasingly common clinical and public health challenge, yet undergraduate medical education (UME) frequently emphasizes single-disease models. Using Cultural Historical Activity Theory (CHAT), this review maps how multimorbidity is conceptualized within UME to inform more deliberate teaching and curriculum design.
METHOD
The authors conducted a scoping review to map the existing medical education literature on multimorbidity in the undergraduate setting. Search terms were developed using a Cochrane Multimorbidity Review as a foundation. Five databases were searched from inception to March 31, 2026: EMBASE, MEDLINE, CINAHL, ERIC and Web of Science. Data extraction was informed by CHAT, and findings were organized on whether articles positioned multimorbidity as a central or peripheral phenomenon of interest.
RESULTS
A total of 3636 articles were identified; 348 underwent full-text review, and 30 met inclusion criteria. Included publications comprised original research (17 [56.7%]), conference abstracts (5 [16.7%]), innovation reports (3 [10%]), opinion pieces (3 [10%]), one short communication, and one letter. Sixteen articles (53.3%) discussed multimorbidity as a central phenomenon of interest, while 14 (46.7%) addressed it as a peripheral phenomenon. When discussed as a central phenomenon, multimorbidity was consistently conceptualized, primarily addressed in formal classroom settings, frequently linked to clinical reasoning, and described as a curricular challenge. When addressed as a peripheral phenomenon of interest, conceptualizations were variable. Multimorbidity appeared within clinical teaching environments, was connected to diverse skills and topics, but educational initiatives were developed with minimal patient involvement.
CONCLUSIONS
This review reveals a core tension: although multimorbidity is common in clinical practice, its presence in UME remains limited and fragmented. It is inconsistently conceptualized and often disconnected from clinical contexts. Strengthening multimorbidity education through stronger conceptual foundations and better integration across curricula and authentic clinical environments is essential to preparing learners to deliver effective care to people living with multimorbidity.
Cara Bezzina, Marina Politis, N. Szmidt et al.· Academic medicine : journal...· 0 citations