Evidence map›Paper›PMID 42110918›Full record

ArticleInternational journal of nursing studies advances2026

Intelligent technologies in operating room nursing: A bibliometric analysis of research.

Yang Yu, Zhili Rao, Wen Zheng, Ting Su, Xin Wen, Lijun Yao, Weixi Zheng, Ying Wang, Xiaofang Chen, Yaling Wang

Abstract read
In one paragraph

Article in International journal of nursing studies advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Yang YuDepartment of Operating Room, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.
Zhili RaoDepartment of Operating Room, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.
Wen ZhengEmergency Department, Fujian Provincial Children's Hospital, Fujian Children's Hospital (Fujian Branch of Shanghai Children's Medical Center), College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, China.
Ting SuDepartment of Operating Room, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.
Xin WenDepartment of Operating Room, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.
Lijun YaoDepartment of Operating Room, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.
Weixi ZhengDepartment of Operating Room, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.
Ying WangDepartment of Operating Room, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.
Xiaofang ChenDepartment of Operating Room, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.
Yaling WangDepartment of Operating Room, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The integration of intelligent technologies in operating room nursing represents a rapidly evolving field. Intelligent operating room nursing refers to the application of artificial intelligence, robotic systems, smart sensing, and data-driven decision support tools within the intraoperative setting to assist perioperative nurses in risk identification, workflow optimization, real-time clinical monitoring, and individualized patient care. Despite growing technological adoption, systematic understanding of the research landscape, knowledge structure, and developmental trajectories remains limited. Objective: We aimed to analyze comprehensively worldwide research trends, identify key contributors, and reveal emerging developments in intelligent operating room nursing through bibliometric analysis. Information sources: Web of Science, Scopus, PubMed, Embase, Cochrane Library, and CINAHL were searched from January 2015 to April 2025. Methods: Following predefined inclusion and exclusion criteria based on relevance to intelligent operating room nursing and publication quality, 291 eligible articles were analyzed using R software with bibliometrix package, VOSviewer, and CiteSpace for bibliometric analysis, network visualization, and thematic evolution assessment. Results: Publication output increased from 16 articles in 2015 to 49 in 2024, with 45.4% of total publications concentrated between 2022 and 2024. The United States dominated with 132 publications, followed by Germany and the United Kingdom (33 each). The Technical University of Munich led institutional contributions with 7 publications. Keyword analysis revealed evolution from an early focus on operating room scheduling and multi-objective optimization (2017) to deep learning applications (between 2020 and 2023), natural language processing (2023), and robotic scrub nurse development (between 2023 and 2025). Six major research themes emerged: Surgical Robotics & Equipment, Nursing Staff & Scheduling, Communication & Collaboration, Information Systems & Data Management, Intelligent Algorithms & Decision Support, and Safety, Quality, & Risk Management. Conclusions: The field demonstrated rapid growth with clear thematic evolution toward nursing-centered intelligent automation and human-machine collaboration systems. Future researchers should prioritize comparative effectiveness studies, implementation strategies, and global health equity considerations to ensure widespread clinical translation. Registration: Not applicable. Social media abstract: Global bibliometric analysis (between 2015 and April 2025) revealed rapid growth in intelligent operating room nursing, with robotic surgery, artificial intelligence, and human-machine collaboration as emerging trends.

Indexed as

Artificial intelligenceBibliometric analysisHuman-machine collaborationOperating room nursingPerioperative careRoboticsSurgical robotics

Identifiers

PMID42110918
PMCPMC13157165

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.