ArticleJournal of nursing management2026
An Evaluation Framework for Large Language Models in Clinical Nursing: A Scoping Review and Expert Consultation.
Article in Journal of nursing management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
aimTo examine the current state of large language model (LLM) evaluation in clinical nursing, synthesize the evaluation dimensions and metrics reported in the literature, and develop a preliminary evaluation framework for LLMs in clinical nursing through expert consultation.
backgroundThe advancement of artificial intelligence products, exemplified by LLMs, has generated excitement about their potential applications in clinical nursing practice, but their effectiveness remains uncertain.
methodsA scoping review was conducted in accordance with Arksey and O'Malley's framework and incorporated experts' consultation. A literature search was conducted across Web of Science, PubMed, Embase, and the Cochrane Library, from their inception to June 21, 2025. Three expert meetings involving eight experts were conducted between August and October 2025 to synthesize evaluation frameworks and scenarios.
resultsA total of 42 studies were included, and the GPT family was the most frequently evaluated. Thirty-seven evaluation metrics were extracted and refined through expert consultation into six primary domains: performance and accuracy, clinical validity and safety, workflow integration and efficiency, usability and user experience, model reliability and ethical considerations, and competency development. A scenario classification and a proposed minimum technical reporting checklist were also developed to support the transparent and comparable evaluation of LLMs in clinical nursing.
conclusionThis study used a scoping review and expert consultation to summarize contemporary literature on LLM evaluations in clinical nursing practice. It provides a preliminary, structured basis for developing and refining a standardized evaluation framework. IMPLICATIONS FOR NURSING MANAGEMENT: This study highlights the need for systematic and context-sensitive evaluation of LLMs in clinical nursing. The findings provide nursing managers with a structured reference for identifying core evaluation dimensions, interpreting evidence, and planning the evaluation and deployment of nursing-specific LLM applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.