Aug 2026· International Journal of Medical Informatics· Vol 220, pp.
106648
· 0 citations· 35 references
Medicine
TL;DR
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.
Abstract
Background
Generative artificial intelligence, particularly large language models (LLMs), has rapidly advanced and shows promise in healthcare for supporting teams through their ability to understand and generate medical text. While human-AI collaboration has been explored, the integration of LLMs into healthcare teams remains under-researched.
Objective
This scoping review aims to examine how LLMs are currently used to support teamwork and communication in healthcare teams, including both solely professional teams and those involving patients.
Methods
Following PRISMA-ScR guidelines, we registered our review with the Open Science Framework (July 30, 2025). We searched PubMed, Web of Science, and ScienceDirect for articles from 2014 to 2024. After screening 3,865 unique titles and abstracts, 127 full texts were reviewed.
Results
Twenty studies were included, predominantly employing quantitative and simulation-based designs, with limited in situ evaluations. LLM use cases were categorized into decision support, communication, and administrative functions. Outcome measures primarily focused on accuracy and quality (15/20 studies), with fewer assessing safety (4/20), readability or empathy (5/20), workflow efficiency (3/20), and error modes (2/20). Across use cases, LLMs demonstrated potential to improve efficiency and communication, although performance and risks varied by task complexity and use context.
Conclusion
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. Current studies largely evaluate model performance without considering the dynamics of human team members. Future research should examine LLMs' impact on trust, collaboration, and decision-making within clinical teams, while implementation efforts must address contextual and interdisciplinary factors to ensure responsible integration.
A scoping review that maps approaches for evaluation of LLM-based applications used for health purposes by nonprofessionals and will provide directional guidance for further research and development in the field of quality assurance for LLM-based applications used by nonprofessionals is outlined.
Maren Keuchel, Pinar Bisgin, Tom Strube et al.· JMIR Research Protocols· 0 citations
Multilingual AI chatbots demonstrate a boost in healthcare efficiency, a reduction in language barriers, and the promotion of health equity, but exhibit challenges regarding validation, workflow integration, and evaluation standards, along with ethical issues such as privacy and bias.
R. Sharanesha, Deepti Virupakshappa, A. Abushanan et al.· Informatics· 0 citations
It is suggested that the reliability of health care LLM reliability is difficult to evaluate adequately using a single universal standard, and future evaluations of health care LLM reliability need to be guided by standardized evaluation frameworks that reflect domain-specific contexts.
Euijun Yang, S. Ko, Hyekyung Woo· Journal of Medical Internet...· 0 citations
Recent developments in large language models (LLMs) have created new opportunities to support primary care, where much of clinical work is text-mediated. This narrative review synthesizes evidence on LLM applications relevant to primary care workflows and summarizes implementation safeguards. Across studies, the most consistently supported near-term value is workflow augmentation, particularly documentation and inbox management (e.g., drafting portal replies and summarizing information for clinician review) and communication support, where benefits are reported primarily as process endpoints (time, acceptability, perceived communication quality) rather than hard patient outcomes. Evidence for improvements in clinician diagnostic reasoning, treatment planning, and downstream patient outcomes is more limited and context-dependent, and many evaluations remain simulated or conducted in adjacent settings, limiting generalizability to routine primary care. Accordingly, potential roles in population health and cost reduction should be treated as hypothesis-generating and evaluated prospectively. Challenges related to privacy, security, transparency, and model reliability shape organizational governance requirements and evolving regulatory expectations for the clinical use of generative AI in primary care. We emphasize a pragmatic adoption approach: prioritize high-volume, lower-risk clerical and communication workflows; maintain clinician verification and accountability; and apply governance and equity safeguards (e.g. privacy, security, transparency, auditability, monitoring for drift and error) before scale-up. Christof et al. provide a narrative review that synthesizes evidence on LLM applications relevant to primary care workflows and summarizes implementation safeguards. They highlight the remaining need to demonstrate improved patient outcome of clinical improvement in many studies and outline a pragmatic adoption approach in practice.
Michael Christof, Krish Patel, Jiandong Zhou et al.· Communications Medicine· 0 citations
Current evidence suggests that LLMs have considerable potential to support future ICU handoff workflows but substantial evidence gaps remain and prospective evaluation in real-world ICU settings, with clinically meaningful safety metrics and structured communication frameworks, is needed.
S. Bains, Michael R Kolesnikov, Steven Bedrick et al.· Considerations in Medicine· 0 citations
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