Global Research Trends, Hotspots, Impacts, and Emerging Developments in Intelligent Operating Room Nursing: A 10-Year Bibliometric Analysis

Global Research Trends, Hotspots, Impacts, and Emerging Developments in Intelligent Operating Room Nursing: A 10-Year Bibliometric Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Global Research Trends, Hotspots, Impacts, and Emerging Developments in Intelligent Operating Room Nursing: A 10-Year Bibliometric Analysis Yang Yu, Wen Zheng, Ting Su, Xin Wen, Lijun Yao, Weixi Zheng, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7157116/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and Objectives: The integration of intelligent technologies in operating room nursing represents a rapidly evolving field requiring systematic analysis to understand global research trends and development patterns. This study aimed to comprehensively analyze worldwide research trends, identify key contributors, and reveal emerging developments in intelligent operating room nursing through bibliometric analysis. Methods: A comprehensive literature search was conducted across six international databases (Web of Science, Scopus, PubMed, Embase, Cochrane Library, and CINAHL) from January 2015 to April 2025. Following rigorous screening criteria, 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 in 2022-2024. The United States dominated with 132 publications, followed by Germany and the United Kingdom (33 each). Technical University of Munich led institutional contributions with 7 publications. Six major research themes emerged: operating room management, robotic technology, nursing practice, patient safety, quality improvement, and intelligent algorithms. Keyword analysis revealed evolution from early focus on operating room scheduling and multi-objective optimization (2017) to deep learning applications (2020-2023), natural language processing (2023), and robotic scrub nurse development (2023-2025). Conclusions: The field demonstrates rapid growth with clear thematic evolution toward human-machine collaboration systems. Future research should prioritize comparative effectiveness studies, implementation science methodologies, and global health equity considerations to ensure widespread clinical translation. Nursing General Surgery Operating room intelligence Intelligent nursing Robotic surgery Artificial intelligence Bibliometric analysis Smart healthcare Perioperative care Human-machine collaboration 1. INTRODUCTION Over the course of several decades, intelligent healthcare systems have evolved to incorporate increasingly sophisticated technologies that enhance human capabilities in clinical settings[ 1 – 3 ]. Similar to other healthcare domains, intelligent operating room nursing encompasses numerous specialized areas, including smart monitoring systems, predictive analytics, and automated documentation[ 4 ]. The utilization of real-time data to identify patterns that can be applied to evaluate patient status is known as intelligent monitoring[ 5 , 6 ]. Instead of relying on manual observation alone, these intelligent tools can be actively integrated into perioperative nursing workflows to personalize patient care during surgery[ 7 – 9 ]. A system's ability to capture and process information from various operating room sensors and equipment is known as integrated data management[ 6 ], creating new possibilities for patient monitoring, procedural efficiency, and safety enhancement[ 9 , 10 ]. Technology-driven innovations are increasingly implemented in surgical workflow management, intraoperative decision support, and resource optimization, enabling more responsive care coordination, improved surgical outcomes, and reduced procedural complications, advancing perioperative practice toward data-informed interventions, enhanced team communication, and optimized patient safety protocols[ 11 ]. As intelligent technologies continue to reshape surgical environments, understanding the evolution of smart systems research in operating room nursing is essential for identifying development trajectories, implementation challenges, and pioneering institutions in this emerging field[ 12 , 13 ]. Bibliometric analysis provides a powerful methodological framework for comprehensively examining global research developments, detecting emerging areas, and identifying influential studies across various surgical specialties[ 14 – 17 ]. Previous bibliometric investigations have explored digital technologies or smart systems in general nursing or surgical contexts, or institution-specific implementations, but the global and thorough assessments of intelligent technology integration in operating room nursing remain scarce[ 18 – 20 ]. This study presents a comprehensive worldwide analysis of intelligent technology research trends in operating room nursing from 2015 to 2025. This study aims to (1) evaluate annual publication output and citation patterns, (2) identify key contributors including prominent researchers, countries, journals, and institutions, and (3) reveal current research frontiers in the application of intelligent technologies in operating room nursing by examining author keywords and research themes. Through a comprehensive and data-driven analysis of intelligent operating room literature, this study provides valuable insights for clinical nursing researchers, surgical teams, hospital management, and medical device manufacturers. Understanding these evolving research patterns can guide future technological developments, ensuring that smart operating room innovations continue to enhance surgical outcomes, improve patient care, and optimize perioperative workflows. 2. METHODS 2.1 Data sources and search strategy We conducted a comprehensive literature search across multiple international databases to ensure comprehensive coverage of relevant publications. The English literature search was performed on the Web of Science Core Collection (WoSCC) database ( https://www.webofscience.com/wos/woscc/basic-search ), Scopus database ( http://www.scopus.com ), PubMed database ( https://pubmed.ncbi.nlm.nih.gov/ ), Embase database ( https://www.embase.com/ ), Cochrane Library ( https://www.cochranelibrary.com/ ), and CINAHL Complete database ( https://www.ebscohost.com/nursing/products/cinahl-databases ). The search strategy employed was "(TS = (smart operating room) OR TS = (intelligent operating room) OR TS = (operating room intelligence) OR TS = (OR automation) OR TS = (surgical robotics) OR TS = (operating theatre intelligence)) AND (TS = (artificial intelligence) OR TS = (machine learning) OR TS = (deep learning) OR TS = (automation) OR TS = (digital surgery))" for relevant publications. The reference types included "article" and "review" publications. The temporal scope encompassed publications from "January 1, 2015–April 31, 2025". All data were acquired on May 1, 2025, to minimize bias caused by database updates. A comprehensive search of multiple databases yielded a total of 923 publications. The database sources included Web of Science (179 articles), CINAHL (65 articles), COCHRANE (136 articles), EMBASE (78 articles), PubMed (157 articles), and Scopus (308 articles). Following the initial search, a rigorous screening process was conducted using specific exclusion criteria (Fig. 1). A total of 632 articles were excluded: 386 duplicates, 17 non-English articles, 11 withdrawn publications, and 219 articles unrelated to the research topic. The remaining 291 eligible articles were imported into EndNote for data cleaning and further analysis. Subsequently, bibliometric and visualization analyses were performed across multiple dimensions, including publications, citations, countries, institutions, journals, and authors. This systematic approach ensured a comprehensive evaluation of the current research landscape in intelligent operating room systems[ 21 ]. 2.2 Inclusion and exclusion criteria Inclusion criteria: Articles or reviews published in peer-reviewed journals and indexed in at least one of the following databases: Web of Science Core Collection (WoSCC), Scopus, PubMed, Embase, Cochrane Library, and CINAHL Complete. Exclusion criteria: (1) Non-English publications, (2) proceeding papers, early access articles, meeting abstracts, editorial materials, letters, book chapters, corrections, news items, reprints, or retracted publications, and (3) duplicate literature across databases. 2.3 Validation of Data To validate the data, title searches were performed to confirm the absence of false positives, with assistance from two colleagues in the medical fields. The retrieved articles were cross-referenced with highly cited publications in Google Scholar to enhance the comprehensiveness of the analysis. The consistency of the metadata and alignment with active journals and prolific authors further confirmed the credibility of the search approach[ 22 ]. 2.4 Study Selection and Bias Information such as the title, authors, institutions, document type, journal, DOI, abstract, publication year, organization, citations, keywords, open-access status, and funding details were extracted and saved as a CSV file for subsequent analysis. Articles were screened to exclude those out of scope, and any missing information was completed. Duplicate records were removed based on the title and DOI using EndNote. To minimize potential bias, articles were ranked by citations, and comprehensive relevance screening was conducted to ensure accuracy and mitigate selection bias. 2.5 Scientific Literature Bibliometric Indicators The cleaned dataset was exported to Microsoft Excel 365 and analyzed using R software (v.4.3.0) with the "bibliometrix" package. Advanced bibliometric indicators were calculated, including the total number of publications (TP), total citations (TC), average citations (AC), number of contributing authors (NCA), annual collaboration index (ACI), number of cited publications (NCP), citations per cited publication (CCP), collaboration index (CI), collaboration coefficient (CC), number of active years of publication (NAY), productivity per active year of publication (PAY), average citation per year (AC/Y), and author indices (h-index, g-index, and m-index). These indicators were measured and compared by publication year and citation patterns. 2.6 Software for bibliometric analysis Bibliometric analysis employs mathematical and statistical methods to analyze literature database data, including publications, authors, institutions, and citations, generating knowledge maps to understand research landscapes and trends. Literature management was conducted using EndNote 21 for merging datasets, screening publications, and removing duplicates. Statistical analyses were performed using R software (version 4.3.0) with Bibliometrix, iGraph, and ggplot2 packages for bibliometric analysis, network visualization, and temporal trend analysis[ 23 , 24 ]. Advanced visualizations were conducted using VOSviewer (1.6.20) and CiteSpace (5.7.R5). VOSviewer analyzed co-authorship networks among countries, institutions, journals, and authors, as well as keyword co-occurrence patterns[ 25 ]. CiteSpace performed burst detection analysis of keywords and literature co-citation analysis[ 26 ]. Thematic analysis employed bibliometric algorithms to categorize research themes into clusters based on conceptual similarity. Temporal evolution analysis tracked theme development across 2015–2025, calculating annual frequencies to identify research patterns. Parameters were optimized for each approach: VOSviewer used full counting method with maximum 25 countries per article; CiteSpace employed time span 2015.01–2025.04 with annual slices, g-index25 (k = 25), LRF = 3.0, LBY = 8, e = 2.0; thematic analysis used adjusted keyword co-occurrence thresholds and semantic similarity algorithms for cluster identification. 3. RESULTS 3.1 Temporal Distribution Analysis The temporal distribution of publications on operating room automation research demonstrates notable trends in research activity. As illustrated in Fig. 2, publication output displays a fluctuating pattern with an overall upward trajectory from 2015 to 2024. The number of publications increased from 16 in 2015 to a peak of 49 in 2024, with 13 publications already recorded in the first four months of 2025 (Table 1 ). Particularly significant is the substantial growth observed during 2022–2024, when 132 articles were published (39 + 44 + 49), representing 45.4% of the total literature corpus of 291 publications. The research field experienced remarkable growth spurts, particularly in 2018 (100% growth rate), 2022 (69.57% growth rate), and steady increases in 2023–2024 (12.82% and 11.36% respectively). The early 2025 data (January-April) shows 13 publications, indicating sustained research momentum in the field. Table 1 Annual Publication Trends in Operating Room Automation Research (2015–2025) Year Number of Publications Cumulative Publications Growth Rate (%) 2015 16 16 — 2016 21 37 31.25 2017 12 49 -42.86 2018 24 73 100.00 2019 29 102 20.83 2020 21 123 -27.59 2021 23 146 9.52 2022 39 185 69.57 2023 44 229 12.82 2024 49 278 11.36 2025 13 291 -73.47 3.2 Journal Distribution and Academic Impact Analysis A total of 291 articles related to smart operating room research were published across 210 journals. Bradford's Law analysis revealed a typical core-periphery structure: the core zone contained 29 journals publishing 98 articles (33.7%), Zone 2 included 85 journals with 97 articles (33.3%), and Zone 3 comprised 96 journals with 96 articles (33.0%) (Fig. 3B, 3C, ). The International Journal of Computer Assisted Radiology and Surgery had the highest publication output (9 articles), followed by the Journal of Robotic Surgery (8 articles) and Lecture Notes in Computer Science (6 articles) (Table 2 , Fig. 3A). Regarding academic impact, Human Factors showed the highest average citations per article (27.67), followed by Anesthesia and Analgesia (4.38) and IJCARS (4.0), with an overall field average of 3.67 citations per article (Fig. 3D). Journal co-citation network analysis revealed that 29 core journals formed four major academic clusters with 295 linkages and a total link strength of 320.75, primarily comprising clinical medicine journals (red cluster), computer informatics journals (green cluster), and intelligent technology journals (blue cluster), with the Journal of Robotic Surgery and IJCARS serving as central nodes that bridge interdisciplinary connections (Fig. 3E). Table 2 Comprehensive Analysis of Top Journals (2015–2025) Rank Journal Publications Degree_Centrality Last_Year 1 INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 9 0.964 2024 2 JOURNAL OF ROBOTIC SURGERY 8 1 2024 3 LECTURE NOTES IN COMPUTER SCIENCE 6 0.821 2024 4 CURRENT DIRECTIONS IN BIOMEDICAL ENGINEERING 5 0.75 2024 5 STUDIES IN HEALTH TECHNOLOGY AND INFORMATICS 5 0.929 2024 6 APPLIED ERGONOMICS 4 0.964 2025 7 JOURNAL OF MEDICAL SYSTEMS 4 0.893 2025 8 JOURNAL OF UROLOGY 4 0.821 2021 9 UROLOGY 4 0.964 2023 10 APPLIED CLINICAL INFORMATICS 3 0.75 2021 11 BMJ OPEN 3 0.607 2021 12 COMPUT INTELL NEUROSCI 3 0.571 2022 13 GYNECOLOGIC ONCOLOGY 3 0.964 2019 14 HUM FACTORS 3 0.857 2022 15 J ROBOT SURG 3 0.929 2024 16 PEDIATRIC ANESTHESIA 3 0.679 2023 17 PLOS ONE 3 0.786 2024 18 SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES 3 0.429 2024 19 ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2 0.786 2019 20 AMERICAN JOURNAL OF INFECTION CONTROL 2 - 2024 21 AMERICAN JOURNAL OF SURGERY 2 0.393 2023 22 ANESTHESIA AND ANALGESIA 2 0.571 2022 23 ANNALS OF SURGERY 2 0.643 2023 24 CIN-COMPUTERS INFORMATICS NURSING 2 0.571 2016 25 FRONT DIGIT HEALTH 2 0.679 2024 26 IFMBE PROCEEDINGS 2 0.714 2018 27 INT J COMPUT ASSIST RADIOL SURG 2 0.393 2024 28 INTERNATIONAL JOURNAL OF GYNECOLOGICAL CANCER 2 0.25 2023 29 INTERNATIONAL JOURNAL OF MEDICAL ROBOTICS AND COMPUTER ASSISTED SURGERY 2 0.75 2019 30 JAMIA OPEN 2 0.643 2023 3.3 Geographical Distribution and International Collaboration Patterns According to the literature analysis, the field of operating room intelligence demonstrates distinct geographical distribution characteristics and international collaboration patterns (Table 3 , Fig. 4). The United States leads with 109 publications, followed by the United Kingdom (33), Germany (33), and China (28) (Table 2 , Fig. 4A). VOSviewer-based international collaboration network analysis reveals a cooperative network encompassing 25 countries/regions with 7 clusters and a total link strength of 32 (Fig. 4B), where the United States occupies the core position (weight: 109, links: 14) as the most important international collaboration hub, while China serves as a major Asian participant (weight: 28, links: 4) primarily collaborating with the US, UK, Japan, and other countries. The global publication distribution heat map (Fig. 4C) further identifies three major research hotspots centered on North America (US), Europe (UK and Germany), and East Asia (China and Japan), with the network exhibiting a "core-periphery" structure characterized by geographical clustering, reflecting a regionalized collaboration model dominated by developed countries with gradually increasing participation from emerging economies. Table 3 Comprehensive Country Analysis of Operating Room Intelligence Research (2015–2025) Rank Country Publications Degree Centrality Betweenness Centrality 1 USA 109 26 23.85 2 GERMANY 33 8 24.24 3 UNITED KINGDOM 33 19 57.58 4 CHINA 28 5 17.86 5 JAPAN 15 7 46.67 6 INDIA 12 7 58.33 7 AUSTRALIA 8 2 25 8 CANADA 8 4 50 9 FRANCE 8 4 50 10 CHINA(TAIWAN) 7 3 42.86 11 ITALY 7 2 28.57 12 SOUTH KOREA 6 4 66.67 13 FINLAND 5 1 20 14 IRELAND 5 0 0 15 AUSTRIA 4 2 50 16 NETHERLANDS 4 3 75 17 SWEDEN 4 2 50 18 SWITZERLAND 4 3 75 19 TURKEY 4 1 25 20 BRAZIL 3 1 33.33 21 ISRAEL 3 3 100 22 POLAND 3 1 33.33 23 SINGAPORE 3 2 66.67 24 BELGIUM 2 1 50 25 DENMARK 2 0 0 26 SPAIN 2 0 0 27 CZECH REPUBLIC 1 1 100 28 GREECE 1 1 100 29 NORWAY 1 0 0 3.4 Institutional Distribution and Collaboration Patterns Institutional analysis revealed that smart operating room research is predominantly concentrated in prestigious universities and medical institutions in Europe and North America (Table 4 , Fig. 5A). Technical University of Munich had the highest publication output (7 articles), followed by Department of Surgery (6 articles), Purdue University (5 articles), and Duke University (4 articles). Among the top 20 productive institutions, most published 3 articles, reflecting the relatively dispersed nature of research in this field. From a temporal perspective, Department of Surgery demonstrated the longest research span (2016–2025, 5 active years), while Technical University of Munich, despite having the highest publication count, showed a shorter research period (2022–2024, 3 active years), indicating the emergence of new research hotspots in the field. Institutional collaboration network analysis revealed that 39 major institutions formed 14 collaborative clusters with 39 linkages and a total link strength of 58, primarily comprising clusters of US East Coast medical schools (red cluster), European technical universities (blue cluster), and clinical medical institutions (green cluster), with Technical University of Munich and Department of Surgery serving as key nodes connecting research forces across different regions and disciplines (Fig. 5B). Table 4 Comprehensive Analysis of Top Institutions (2015–2025) Rank Short_Name Publications Degree_Centrality Active_Years 1 TECHNICAL UNIVERSITY OF MUNICH 7 0.026 3 2 DEPARTMENT OF SURGERY 6 0.026 5 3 PURDUE UNIV 5 0.053 4 4 DUKE UNIV 4 0.026 3 5 BRIGHAM & WOMENS HOSP 3 0.132 3 6 CAIRO UNIV 3 0.026 3 7 CLEMSON UNIV 3 0.079 1 8 DEPT OBSTET & GYNECOL 3 0.053 3 9 DEPT SURG 3 0.079 3 10 HANNOVER MEDICAL SCHOOL 3 0.026 3 11 HARVARD MED SCH 3 0.132 3 12 INDIANA UNIV SCH MED 3 0.053 2 13 MASSACHUSETTS GEN HOSP 3 0.105 3 14 MAYO CLIN 3 0.053 3 15 NINGBO UNIVERSITY 3 - 1 16 RUSH UNIV 3 - 3 17 TOKYO WOMEN'S MEDICAL UNIVERSITY 3 - 3 18 UNIV LEEDS 3 0.105 3 19 UNIV LEIPZIG 3 3 20 UNIV PITTSBURGH 3 0.026 2 21 UNIVERSITY HOSPITAL RECHTS DER ISAR 3 0.026 3 22 VANDERBILT UNIV 3 - 3 23 HOSPITAL UNIVERSITY 2 - 1 24 BAIRD INST 2 0.026 2 25 CAIRO UNIVERSITY 2 0.026 2 26 CEDARS SINAI MED CTR 2 0.026 2 27 CHAN SCHOOL OF MEDICINE 2 0.053 2 28 CHINA MED UNIV 2 - 1 29 CLEMSON UNIVERSITY 2 0.026 2 30 COLUMBIA UNIV 2 0.079 1 3.5 Author Productivity and Collaboration Network Analysis The distribution of author publications in the field of operating room intelligence exhibits typical long-tail characteristics, conforming to Lotka's Law in bibliometrics. According to author publication statistics (Table 5 ), WILHELM D leads with 7 publications, establishing himself as the most influential scholar in this field; BERNHARD L follows closely with 6 publications; WAGNER L and XIANG W tie for third place with 5 publications each. Additionally (Fig. 6A). Lotka's Law validation results show an author productivity distribution slope of -3.915, indicating that a small number of highly productive authors contribute a large volume of literature while most authors publish only 1–2 articles (Fig. 6B). The author collaboration network analysis constructed using VOSviewer software reveals a network comprising 6 main clusters with a total link strength of 32, where WILHELM D serves as the network's core node with 14 connections, making him the most important collaboration hub in this field (Fig. 6C). The collaboration network exhibits distinct clustering characteristics, with the blue cluster centered on WILHELM D including scholars such as BERNHARD L, WAGNER L, KOLB S, and JELL A, forming the most tightly connected research team. Other clusters are represented in different colors, including the red cluster (ABRAHAM J, MIGUCHI M, etc.) and the green cluster (BEN ABDALLAH A, KRONZER A, etc.), reflecting the diversified research team organizational patterns and relatively stable academic collaboration relationships in this field. Table 5 Productivity and Network Centrality (2015–2025) Rank Author Publications Citations Degree Centrality 1 WILHELM D 7 2 5 2 BERNHARD L 6 2 5 3 WAGNER L 5 2 5 4 XIANG W 5 14 0 5 ABRAHAM J 4 8 3 6 WACHS JP 4 15 1 7 ZHOU T 4 15 1 8 CATCHPOLE K 3 4 1 9 CAVUOTO L 3 71 5 10 GURU KA 3 73 3 11 HUSSEIN AA 3 73 3 12 JELL A 3 0 5 13 KANNAMPALLIL T 3 8 3 14 KOLB S 3 2 5 15 LI Z 3 1 0 16 MASAMUNE K 3 5 1 17 MURAGAKI Y 3 5 1 18 NEUMUTH T 3 7 1 19 STEFANIDIS D 3 64 2 20 YU D 3 64 2 21 AHMAD B 2 2 3 22 ALVARADO N 2 11 0 23 ANDERSEN B 2 1 1 24 ANGER JT 2 7 1 25 AVIDAN MS 2 6 3 26 BANNON PG 2 11 0 27 BATES DW 2 15 0 28 BEN ABDALLAH A 2 8 3 29 BERLET M 2 2 5 30 BONIDIE M 2 18 0 3.6 Keyword Analysis and Research Hotspot Evolution Keyword analysis revealed core themes and developmental trends in smart operating room research (Table 6 , Fig. 7). Among 100 high-frequency keywords, "OPERATING ROOM" had the highest occurrence (100 times), followed by "ROBOTIC SURGERY" (89 times), "NURSING" (67 times), "TEAMWORK & COMMUNICATION" (44 times), and "ARTIFICIAL INTELLIGENCE" (40 times), reflecting the field's emphasis on both technological applications and humanistic care (Fig. 7A). Regarding network centrality, "ROBOTIC SURGERY" demonstrated the highest degree centrality (0.863) and strength centrality (0.756), with "NURSING" (degree centrality 0.897, strength centrality 0.801) following closely, indicating these topics occupy central positions in the knowledge network. Keyword co-occurrence network analysis showed that 50 core keywords formed six major research clusters with 585 linkages and a total link strength of 1467, primarily focusing on operating room management, robotic technology, nursing practice, patient safety, quality improvement, and intelligent algorithms (Fig. 7B). Citation burst analysis identified 23 keywords with burst characteristics, with early hotspots including "operating room scheduling" (2017), and "multi-objective optimization" (2017), while recent emerging trends encompass "deep learning" (2020–2023), "natural language processing" (2023), and "robotic scrub nurse" (2023–2025) (Fig. 7C, 7D). Table 6 Comprehensive Analysis of Top Keywords (2015–2025) Rank Keyword Frequency Degree_Centrality Strength_Centrality 1 OPERATING ROOM 100 1 1 2 ROBOTIC SURGERY 89 0.863 0.756 3 NURSING 67 0.897 0.801 4 TEAMWORK & COMMUNICATION 44 0.502 0.357 5 ARTIFICIAL INTELLIGENCE 40 0.507 0.315 6 SURGICAL INSTRUMENTS 33 0.295 0.225 7 LAPAROSCOPIC SURGERY 32 0.552 0.365 8 ROBOTICS 31 0.542 0.393 9 EFFICIENCY 28 0.583 0.393 10 PATIENT SAFETY 26 0.495 0.335 11 SURGEON 16 0.517 0.376 12 *ROBOTIC SURGICAL PROCEDURES/METHODS 12 0.195 0.103 13 PROSPECTIVE STUDY 12 0.45 0.314 14 WORKFLOW 12 0.322 0.181 15 *ROBOTICS 11 0.12 0.07 16 ERGONOMICS 11 0.212 0.119 17 OPERATING-ROOM 11 0.138 0.076 18 OUTCOMES 11 0.132 0.065 19 PERFORMANCE 11 0.145 0.083 20 SURGEONS 11 0.258 0.155 21 COMPLICATION 10 0.318 0.23 22 DB - SCOPUS 10 0.34 0.202 23 OPERATING ROOM PERSONNEL 10 0.308 0.183 24 PERIOPERATIVE NURSING 10 0.268 0.164 25 RANDOMIZED CONTROLLED TRIAL 10 0.38 0.281 26 ROBOTIC SCRUB NURSE 10 0.117 0.076 27 SAFETY 10 0.152 0.087 28 SIMULATION 10 0.228 0.122 29 BODY MASS 9 0.35 0.238 30 CLINICAL ARTICLE 9 0.222 0.135 31 DEEP LEARNING 9 0.105 0.062 32 EDUCATION 9 0.202 0.129 33 HOSPITALS 9 0.162 0.085 34 IMPACT 9 0.11 0.062 35 OPERATING ROOM NURSING 9 0.242 0.152 36 PERIOPERATIVE CARE 9 0.135 0.07 37 PROSTATECTOMY 9 0.21 0.135 38 QUALITY IMPROVEMENT 9 0.162 0.09 39 *ROBOTIC SURGICAL PROCEDURES 8 0.085 0.046 40 CARE 8 0.092 0.055 41 DIAGNOSIS 8 0.25 0.171 42 HUMAN FACTORS 8 0.095 0.051 43 SURVEYS AND QUESTIONNAIRES 8 0.208 0.112 44 TRANSPLANTATION (SURGICAL) 8 0.17 0.097 45 ALGORITHMS 7 0.11 0.059 46 DECISION MAKING 7 0.142 0.076 47 GYNECOLOGY 7 0.218 0.121 48 HYSTERECTOMY 7 0.2 0.116 49 IMPLEMENTATION 7 0.065 0.032 50 LEARNING CURVE 7 0.212 0.116 3.7 Thematic Evolution and Research Development Trends Thematic evolution analysis identified six core research clusters in the smart operating room field: Surgical Robotics & Equipment, Nursing Staff & Scheduling, Surgical Settings & Workflow, Safety Quality & Risk Management, Intelligent Algorithms & Decision Support, Communication & Collaboration, and Information Systems & Data Management (Fig. 8A). The thematic relationship network revealed that Surgical Robotics & Equipment represents the largest cluster with the strongest inter-thematic connections, while Communication & Collaboration and Intelligent Algorithms & Decision Support developed relatively independently. Temporal evolution trends showed dynamic characteristics across 2015–2025: Surgical Robotics & Equipment peaked in 2019 (33 occurrences), Surgical Settings & Workflow was most active in 2019 (28 occurrences), Nursing Staff & Scheduling experienced significant growth in 2019 (25 occurrences), while Intelligent Algorithms & Decision Support started strong in 2015 (10 occurrences) before stabilizing (Fig. 8B). Heat map analysis further revealed that Surgical Robotics & Equipment continuously intensified during 2016–2019, Nursing Staff & Scheduling experienced concentrated emergence in 2018–2019, and Safety Quality & Risk Management has gained increasing importance in recent years, reflecting the field's evolution from technology-oriented towards human-machine collaboration and integrated quality-safety considerations (Fig. 8C, 8D). 4. DISCUSSION By examining relevant literature, we conducted a comprehensive bibliometric analysis of smart OR nursing studies[ 27 , 28 ]. The bibliometric patterns observed in these studies provide insights into the opportunities and barriers facing the integration of smart technologies into OR nursing[ 29 ], suggesting key implementation challenges and identifying impediments that may prevent the translation of research findings into clinical practice. 4.1 Temporal Distribution and Journal Publication Patterns Publications grew from 16 (2015) to 49 (2024), with 2022–2024 representing 45.4% of total output (132 out of 291 publications). The field demonstrated significant growth momentum, particularly with notable increases in 2018 (100% growth rate), 2022 (69.57% growth rate), and sustained growth in 2023–2024 (12.82% and 11.36% respectively). Concurrently, 291 articles scattered across 210 journals following Bradford's Law, with International Journal of Computer Assisted Radiology and Surgery leading (9 publications) and Human Factors achieving highest impact (27.67 citations per article) against a field average of 3.67. The co-citation network reveals four interdisciplinary clusters bridged by Journal of Robotic Surgery and IJCARS. This temporal-journal evolution demonstrates a coherent pattern of field maturation from concentrated foundational work to diversified specialization. The inverse relationship between growing publication volume and declining citations directly corresponds to the wide journal scatter, where researchers transition from citing core foundational texts to drawing from increasingly diverse and specialized literature across multiple disciplinary venues[ 30 ]. The early research period (2015–2017) established foundational knowledge in operating room automation, while the publication surge in 2022–2024 reflects the field's maturation and practical implementation across diverse healthcare settings. The journal distribution pattern reinforces this evolutionary trajectory. Bradford's Law compliance with relatively even zone distribution indicates that operating room intelligence has not yet consolidated around dominant publication venues, reflecting the field's expansion across traditional disciplinary boundaries[ 31 ]. Human Factors'[ 32 ] exceptional citation performance (27.67) demonstrates that foundational human-centered design principles maintain broad relevance even as research diversifies, while the modest field average (3.67) suggests that specialized technical applications receive more limited cross-field recognition. The four-cluster co-citation structure (clinical medicine, computer informatics, intelligent technology) mirrors the temporal shift toward specialized applications, where researchers naturally gravitate toward domain-specific publication venues. While Journal of Robotic Surgery[ 33 ] [ 34 ]and IJCARS[ 35 , 36 ] serve as crucial interdisciplinary bridges, this fragmentation across both time and publication landscape creates knowledge silos that challenge integrated field development. The temporal concentration of recent publications combined with journal dispersion suggests the field requires new mechanisms for knowledge synthesis to maintain coherent advancement despite increasing specialization. 4.2 Geographic Distribution and Institutional Concentration Patterns The dominance of developed countries—particularly the US (109 publications), UK (33), and Germany (33)—directly corresponds to institutional concentration among prestigious technical universities and medical centers, with Technical University of Munich (7 publications), Purdue University (5), and Duke University (4) leading research output. Meanwhile, emerging economies like China, despite substantial research capacity (28 publications), maintain more limited international connections (4 links) and correspondingly fewer leading institutions in global networks.This geographical-institutional convergence reflects critical infrastructure requirements that extend beyond research capacity alone. Leading countries benefit from established regulatory pathways like the FDA's (Food and Drug Administration) medical device approval framework and tight integration between elite academic institutions, world-class medical centers, and advanced technology companies[ 37 ]. The institutional collaboration network, organized around US East Coast medical schools, European technical universities, and clinical medical institutions, mirrors the geographical clustering pattern where regional proximity facilitates complex interdisciplinary partnerships essential for this field[ 38 , 39 ]. The temporal evolution reveals how institutional and geographical advantages reinforce each other. While established centers like Department of Surgery demonstrate sustained engagement (2016–2025), new hotspots such as Technical University of Munich's intense recent activity (2022–2024) reflect expanding but still concentrated research infrastructure. This pattern suggests that specialized resources—advanced robotics laboratories, clinical testing environments, regulatory expertise, and substantial capital investment—remain concentrated in elite institutions within developed countries, creating barriers for emerging economies to achieve full integration into global research networks[ 40 , 41 ]. However, even these resource-rich geographical regions and institutions face substantial implementation barriers. The relatively dispersed publication pattern among institutions (most contributing only 3 articles) indicates that even well-funded centers struggle to maintain sustained research programs due to high capital costs, complex clinical workflows, and surgical team resistance to automation[ 42 , 43 ]. This suggests that successful operating room intelligence development requires not just technological innovation but fundamental healthcare system transformation—a challenge that transcends individual institutional or national capabilities. 4.3 Author Productivity and Collaboration Network Patterns The author productivity analysis reveals a highly concentrated landscape with WILHELM D leading at 7 publications and serving as the central collaboration hub (14 connections), followed by a small core group including BERNHARD L, WAGNER L, and XIANG W. The Lotka's Law validation (slope = -3.915) confirms extreme concentration where few authors drive knowledge creation while most contribute only 1–2 articles. The collaboration network shows 6 distinct clusters with WILHELM D functioning as the primary knowledge broker. This steep concentration reflects the field's specialized nature, requiring substantial expertise and resources that few researchers can sustain consistently. The pattern indicates operating room intelligence research remains in an early consolidation phase, where pioneer researchers establish foundational knowledge and collaborative frameworks. WILHELM D's central position enables cross-cluster knowledge transfer but creates potential bottlenecks for field advancement[ 44 – 46 ]. The distinct clustering patterns suggest research teams organize around complementary expertise rather than geographical proximity, forming stable collaborative relationships essential for complex interdisciplinary projects. However, this heavy dependence on individual researchers poses both advantages—enabling deep specialization and sustained collaboration—and risks for field development if key contributors become unavailable. 4.4 Keyword Analysis and Thematic Evolution Patterns Among 100 high-frequency keywords, "OPERATING ROOM" leads with 100 occurrences, followed by "ROBOTIC SURGERY" (89), "NURSING" (67), "TEAMWORK & COMMUNICATION" (44), and "ARTIFICIAL INTELLIGENCE" (40). Regarding network centrality, "NURSING" demonstrates the highest degree centrality (0.897) and strength centrality (0.801), with "ROBOTIC SURGERY" following closely (degree centrality 0.863, strength centrality 0.756), indicating these topics occupy central positions in the research landscape. Keyword co-occurrence network analysis revealed that 50 core keywords formed six major research clusters with 585 linkages and a total link strength of 1467, primarily focusing on operating room management, robotic technology, nursing practice, patient safety, quality improvement, and intelligent algorithms. Citation burst analysis identified 23 keywords with burst characteristics, revealing temporal evolution from early focus on "operating room scheduling" (2017) and "multi-objective optimization" (2017) to recent emphasis on "deep learning" (2020–2023), "natural language processing" (2023), and "robotic scrub nurse" (2023–2025). This evolution demonstrates the field's progression through distinct phases: from operating room scheduling and optimization approaches to AI integration, and from technology-centered approaches to human-machine collaboration systems. The high centrality of both "ROBOTIC SURGERY" and "NURSING" reflects the field's recognition that successful automation requires deep clinical workflow integration rather than purely technological solutions[ 47 , 48 ]. The citation burst progression from scheduling optimization to sophisticated AI applications demonstrates accelerating technological adoption, while the emphasis on teamwork, communication, and quality improvement indicates field maturation toward comprehensive human-machine collaborative systems[ 49 ]. 4.5 Future Research Directions and Policy Implications Future research must prioritize comparative effectiveness studies evaluating intelligent technologies' impact on patient outcomes and operational efficiency across diverse healthcare settings. Cross-national validation models should assess technology feasibility while accounting for infrastructure and resource variations. Implementation science methodologies must examine organizational factors affecting adoption, including culture, leadership, and workflow redesign, alongside health equity assessments to prevent exacerbating healthcare disparities[ 50 , 51 ].Global regulatory harmonization is critical for broader implementation. Current frameworks inadequately address AI systems that continuously learn and evolve, requiring new approaches balancing innovation with patient safety. Evidence standards must encompass dynamic AI performance beyond traditional device metrics, while professional scope guidelines should clarify roles in AI-augmented environments and liability frameworks address legal concerns impeding adoption[ 52 , 53 ]. Meanwhile, enhancing collaboration between researchers, clinicians, policymakers, and technology developers is essential. International partnerships should explore culturally appropriate strategies for resource-limited countries, ensuring global healthcare equity. Interdisciplinary collaborations must integrate technical, economic, and organizational expertise, while industry-academia partnerships should develop scalable implementation models adaptable across diverse healthcare contexts. 4.6 Study Limitations This analysis has several limitations affecting implementation planning implications. Academic literature may exhibit publication bias toward positive results, potentially overrepresenting successful implementations while underreporting failed pilots. The focus on English-language publications from developed countries may miss important implementation experiences from diverse healthcare contexts. Additionally, temporal lag in academic publishing means current implementation challenges may not be reflected in peer-reviewed literature, potentially limiting findings' relevance for immediate decision-making. 5. CONCLUSIONS This study explores operating room intelligence research through comprehensive bibliometric analysis, providing an overview of its evolution, identifying emerging trends, and evaluating its progression from 2015–2025. Key research hotspots include robotic surgery, nursing automation, AI integration, and human-machine collaboration systems. The field has evolved from operational optimization (2017) to deep learning applications (2020–2023) and robotic scrub nurse development (2023–2025). Future research should focus on comparative effectiveness studies, cross-national validation models, and implementation science methodologies addressing organizational factors and health equity concerns. International collaboration reveals significant concentration, with the United States leading (109 publications) alongside the UK, Germany, and China. However, emerging economies maintain limited international connections, highlighting global research inequities. Enhanced collaboration between researchers, clinicians, policymakers, and technology developers is essential for translating technological advancements into practical clinical applications. The field faces substantial implementation barriers despite technological progress. High capital costs, complex clinical workflows, and resistance to automation challenge even well-resourced institutions. Moving forward, addressing regulatory harmonization for AI systems, developing evidence standards for dynamic AI performance, and establishing professional scope guidelines will be crucial for broader adoption. Future research should prioritize global health equity, ensuring operating room intelligence benefits diverse healthcare contexts rather than exacerbating existing disparities. Interdisciplinary partnerships must integrate technical, economic, and organizational expertise while developing scalable implementation models. The field requires enhanced knowledge synthesis mechanisms to prevent disciplinary fragmentation and maintain coherent advancement toward comprehensive human-machine collaborative systems that improve patient outcomes and healthcare delivery worldwide. Abbreviations WoSCC: Web of Science Core Collection Declarations Data availability Data materials are real and available. Yaling Wang should be contacted if someone wants to request the data from this study. Acknowledgements Thank you Alain Vandal (Biostatistics & Epidemiology, Auckland University of Technology) for the introduction to RStudio™ and Andrew (Drew) South (Liaison Research Librarian, Auckland University of Technology) for search and Endnote™ support. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Author information Authors and Affiliations Present Address: Department of Nursing, Melbourne School of Health Sciences, 161 Barry St, Carlton, Victoria, 3053, Australia Rebecca J. Jarden School of Clinical Sciences, Auckland University of Technology (AUT), North Shore Campus, 90 Akoranga Drive, Northcote, Auckland, 0627, New Zealand Rebecca J. Jarden, Margaret Sandham & Jane Koziol-McLain School of Engineering, Computing and Mathematical Sciences (D-75), Auckland University of Technology, AUT Tower, 2-14 Wakefield Street, Auckland, 1010, New Zealand Ajit Narayanan School of Clinical Sciences and School of Public Health and Psychosocial Studies, Auckland University of Technology (AUT), North Shore Campus, 90 Akoranga Drive, Northcote, Auckland, 0627, New Zealand Richard J. Siegert Contributions The first author RJ has contributed to this research inception, drafting and editing the manuscript in its entirety. All other authors AN, MS, RS, JKM have contributed to project conception, revising, editing and approving the manuscript in its entirety. All authors read and approved the final manuscript. Corresponding author Correspondence to Rebecca J. Jarden. Ethics declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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S Afr Med J 114(1):22–26 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7157116","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":487423358,"identity":"b1847155-5985-428b-a247-c2cfaf846ce1","order_by":0,"name":"Yang Yu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yu","suffix":""},{"id":487423404,"identity":"bee0fe01-56f8-4e76-9f4d-bd4f02b50658","order_by":1,"name":"Wen Zheng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Zheng","suffix":""},{"id":487431305,"identity":"c04e2d21-ee8c-4e11-8408-4da8ebfbc99a","order_by":2,"name":"Ting Su","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Su","suffix":""},{"id":487431306,"identity":"768ef918-1f6c-4ffa-bc2f-1d91c6e10088","order_by":3,"name":"Xin Wen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wen","suffix":""},{"id":487431307,"identity":"802e6005-2a43-48b0-8114-db76adb4c47c","order_by":4,"name":"Lijun Yao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Yao","suffix":""},{"id":487431308,"identity":"5a9ba6a9-fa9f-4114-aa14-a2a302ddd1db","order_by":5,"name":"Weixi Zheng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Weixi","middleName":"","lastName":"Zheng","suffix":""},{"id":487431309,"identity":"d09685dc-d91a-4b01-946e-7b01b67740d1","order_by":6,"name":"Ying Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Wang","suffix":""},{"id":487431310,"identity":"9e3d6a5a-68ec-43e1-a4cd-a15da55a6368","order_by":7,"name":"Xiaofang Chen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xiaofang","middleName":"","lastName":"Chen","suffix":""},{"id":487431311,"identity":"612e571b-0bdf-420d-81bc-0308eb85ef6f","order_by":8,"name":"Yalin Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYFAC5oYDH35IyMkzMwIZBjZ2RGhhbDw4s8fC2LC9+eDDGQVpycRoaT7MwVaRyHDmWLIxz4dDjA2ENBjcSGw4zMAjkcA4I8dM2sbgADMD++GjGwhqKbCQyGOXAGrJMbjDx8CTlnYDnxYzkJYZPBLFYFtyDJ4xM0jwmBHWwsMmkdhwA6jFwuAwYwPxWkDeZyBGi/2Zhw3AQJaABHKPQVoyGyG/SLYnH/7w4UcdJCp//LGx42c/fAyvFgaBBDQBNrzKQYD/AEElo2AUjIJRMNIBAJzxVHrrHCUUAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Yalin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-07-18 11:19:59","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7157116/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7157116/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87182831,"identity":"8976a3a5-0bee-4c66-be2b-3d9b0a8f5ec4","added_by":"auto","created_at":"2025-07-21 09:58:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1715393,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7157116/v1/c07fa7e3-9b71-47fc-9833-577d9443d498.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGlobal Research Trends, Hotspots, Impacts, and Emerging Developments in Intelligent Operating Room Nursing: A 10-Year Bibliometric Analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eOver the course of several decades, intelligent healthcare systems have evolved to incorporate increasingly sophisticated technologies that enhance human capabilities in clinical settings[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Similar to other healthcare domains, intelligent operating room nursing encompasses numerous specialized areas, including smart monitoring systems, predictive analytics, and automated documentation[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The utilization of real-time data to identify patterns that can be applied to evaluate patient status is known as intelligent monitoring[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Instead of relying on manual observation alone, these intelligent tools can be actively integrated into perioperative nursing workflows to personalize patient care during surgery[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A system's ability to capture and process information from various operating room sensors and equipment is known as integrated data management[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], creating new possibilities for patient monitoring, procedural efficiency, and safety enhancement[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Technology-driven innovations are increasingly implemented in surgical workflow management, intraoperative decision support, and resource optimization, enabling more responsive care coordination, improved surgical outcomes, and reduced procedural complications, advancing perioperative practice toward data-informed interventions, enhanced team communication, and optimized patient safety protocols[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. As intelligent technologies continue to reshape surgical environments, understanding the evolution of smart systems research in operating room nursing is essential for identifying development trajectories, implementation challenges, and pioneering institutions in this emerging field[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBibliometric analysis provides a powerful methodological framework for comprehensively examining global research developments, detecting emerging areas, and identifying influential studies across various surgical specialties[\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Previous bibliometric investigations have explored digital technologies or smart systems in general nursing or surgical contexts, or institution-specific implementations, but the global and thorough assessments of intelligent technology integration in operating room nursing remain scarce[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This study presents a comprehensive worldwide analysis of intelligent technology research trends in operating room nursing from 2015 to 2025. This study aims to (1) evaluate annual publication output and citation patterns, (2) identify key contributors including prominent researchers, countries, journals, and institutions, and (3) reveal current research frontiers in the application of intelligent technologies in operating room nursing by examining author keywords and research themes.\u003c/p\u003e\u003cp\u003eThrough a comprehensive and data-driven analysis of intelligent operating room literature, this study provides valuable insights for clinical nursing researchers, surgical teams, hospital management, and medical device manufacturers. Understanding these evolving research patterns can guide future technological developments, ensuring that smart operating room innovations continue to enhance surgical outcomes, improve patient care, and optimize perioperative workflows.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data sources and search strategy\u003c/h2\u003e\u003cp\u003eWe conducted a comprehensive literature search across multiple international databases to ensure comprehensive coverage of relevant publications. The English literature search was performed on the Web of Science Core Collection (WoSCC) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.webofscience.com/wos/woscc/basic-search\u003c/span\u003e\u003cspan address=\"https://www.webofscience.com/wos/woscc/basic-search\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Scopus database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.scopus.com\u003c/span\u003e\u003cspan address=\"http://www.scopus.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), PubMed database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Embase database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.embase.com/\u003c/span\u003e\u003cspan address=\"https://www.embase.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Cochrane Library (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cochranelibrary.com/\u003c/span\u003e\u003cspan address=\"https://www.cochranelibrary.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and CINAHL Complete database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebscohost.com/nursing/products/cinahl-databases\u003c/span\u003e\u003cspan address=\"https://www.ebscohost.com/nursing/products/cinahl-databases\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe search strategy employed was \"(TS = (smart operating room) OR TS = (intelligent operating room) OR TS = (operating room intelligence) OR TS = (OR automation) OR TS = (surgical robotics) OR TS = (operating theatre intelligence)) AND (TS = (artificial intelligence) OR TS = (machine learning) OR TS = (deep learning) OR TS = (automation) OR TS = (digital surgery))\" for relevant publications. The reference types included \"article\" and \"review\" publications. The temporal scope encompassed publications from \"January 1, 2015\u0026ndash;April 31, 2025\". All data were acquired on May 1, 2025, to minimize bias caused by database updates.\u003c/p\u003e\u003cp\u003eA comprehensive search of multiple databases yielded a total of 923 publications. The database sources included Web of Science (179 articles), CINAHL (65 articles), COCHRANE (136 articles), EMBASE (78 articles), PubMed (157 articles), and Scopus (308 articles). Following the initial search, a rigorous screening process was conducted using specific exclusion criteria (Fig.\u0026nbsp;1). A total of 632 articles were excluded: 386 duplicates, 17 non-English articles, 11 withdrawn publications, and 219 articles unrelated to the research topic. The remaining 291 eligible articles were imported into EndNote for data cleaning and further analysis. Subsequently, bibliometric and visualization analyses were performed across multiple dimensions, including publications, citations, countries, institutions, journals, and authors. This systematic approach ensured a comprehensive evaluation of the current research landscape in intelligent operating room systems[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Inclusion and exclusion criteria\u003c/h2\u003e\u003cp\u003eInclusion criteria: Articles or reviews published in peer-reviewed journals and indexed in at least one of the following databases: Web of Science Core Collection (WoSCC), Scopus, PubMed, Embase, Cochrane Library, and CINAHL Complete.\u003c/p\u003e\u003cp\u003eExclusion criteria: (1) Non-English publications, (2) proceeding papers, early access articles, meeting abstracts, editorial materials, letters, book chapters, corrections, news items, reprints, or retracted publications, and (3) duplicate literature across databases.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Validation of Data\u003c/h2\u003e\u003cp\u003eTo validate the data, title searches were performed to confirm the absence of false positives, with assistance from two colleagues in the medical fields. The retrieved articles were cross-referenced with highly cited publications in Google Scholar to enhance the comprehensiveness of the analysis. The consistency of the metadata and alignment with active journals and prolific authors further confirmed the credibility of the search approach[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Study Selection and Bias\u003c/h2\u003e\u003cp\u003eInformation such as the title, authors, institutions, document type, journal, DOI, abstract, publication year, organization, citations, keywords, open-access status, and funding details were extracted and saved as a CSV file for subsequent analysis. Articles were screened to exclude those out of scope, and any missing information was completed. Duplicate records were removed based on the title and DOI using EndNote. To minimize potential bias, articles were ranked by citations, and comprehensive relevance screening was conducted to ensure accuracy and mitigate selection bias.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Scientific Literature Bibliometric Indicators\u003c/h2\u003e\u003cp\u003eThe cleaned dataset was exported to Microsoft Excel 365 and analyzed using R software (v.4.3.0) with the \"bibliometrix\" package. Advanced bibliometric indicators were calculated, including the total number of publications (TP), total citations (TC), average citations (AC), number of contributing authors (NCA), annual collaboration index (ACI), number of cited publications (NCP), citations per cited publication (CCP), collaboration index (CI), collaboration coefficient (CC), number of active years of publication (NAY), productivity per active year of publication (PAY), average citation per year (AC/Y), and author indices (h-index, g-index, and m-index). These indicators were measured and compared by publication year and citation patterns.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Software for bibliometric analysis\u003c/h2\u003e\u003cp\u003eBibliometric analysis employs mathematical and statistical methods to analyze literature database data, including publications, authors, institutions, and citations, generating knowledge maps to understand research landscapes and trends. Literature management was conducted using EndNote 21 for merging datasets, screening publications, and removing duplicates. Statistical analyses were performed using R software (version 4.3.0) with Bibliometrix, iGraph, and ggplot2 packages for bibliometric analysis, network visualization, and temporal trend analysis[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Advanced visualizations were conducted using VOSviewer (1.6.20) and CiteSpace (5.7.R5). VOSviewer analyzed co-authorship networks among countries, institutions, journals, and authors, as well as keyword co-occurrence patterns[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. CiteSpace performed burst detection analysis of keywords and literature co-citation analysis[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThematic analysis employed bibliometric algorithms to categorize research themes into clusters based on conceptual similarity. Temporal evolution analysis tracked theme development across 2015\u0026ndash;2025, calculating annual frequencies to identify research patterns.\u003c/p\u003e\u003cp\u003eParameters were optimized for each approach: VOSviewer used full counting method with maximum 25 countries per article; CiteSpace employed time span 2015.01\u0026ndash;2025.04 with annual slices, g-index25 (k\u0026thinsp;=\u0026thinsp;25), LRF\u0026thinsp;=\u0026thinsp;3.0, LBY\u0026thinsp;=\u0026thinsp;8, e\u0026thinsp;=\u0026thinsp;2.0; thematic analysis used adjusted keyword co-occurrence thresholds and semantic similarity algorithms for cluster identification.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Temporal Distribution Analysis\u003c/h2\u003e\n \u003cp\u003eThe temporal distribution of publications on operating room automation research demonstrates notable trends in research activity. As illustrated in Fig. 2, publication output displays a fluctuating pattern with an overall upward trajectory from 2015 to 2024. The number of publications increased from 16 in 2015 to a peak of 49 in 2024, with 13 publications already recorded in the first four months of 2025 (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Particularly significant is the substantial growth observed during 2022\u0026ndash;2024, when 132 articles were published (39\u0026thinsp;+\u0026thinsp;44\u0026thinsp;+\u0026thinsp;49), representing 45.4% of the total literature corpus of 291 publications. The research field experienced remarkable growth spurts, particularly in 2018 (100% growth rate), 2022 (69.57% growth rate), and steady increases in 2023\u0026ndash;2024 (12.82% and 11.36% respectively). The early 2025 data (January-April) shows 13 publications, indicating sustained research momentum in the field.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnnual Publication Trends in Operating Room Automation Research (2015\u0026ndash;2025)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of Publications\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCumulative Publications\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGrowth Rate (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-42.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-27.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-73.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Journal Distribution and Academic Impact Analysis\u003c/h2\u003e\n \u003cp\u003eA total of 291 articles related to smart operating room research were published across 210 journals. Bradford\u0026apos;s Law analysis revealed a typical core-periphery structure: the core zone contained 29 journals publishing 98 articles (33.7%), Zone 2 included 85 journals with 97 articles (33.3%), and Zone 3 comprised 96 journals with 96 articles (33.0%) (Fig. 3B, 3C, ). The International Journal of Computer Assisted Radiology and Surgery had the highest publication output (9 articles), followed by the Journal of Robotic Surgery (8 articles) and Lecture Notes in Computer Science (6 articles) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. 3A). Regarding academic impact, Human Factors showed the highest average citations per article (27.67), followed by Anesthesia and Analgesia (4.38) and IJCARS (4.0), with an overall field average of 3.67 citations per article (Fig. 3D). Journal co-citation network analysis revealed that 29 core journals formed four major academic clusters with 295 linkages and a total link strength of 320.75, primarily comprising clinical medicine journals (red cluster), computer informatics journals (green cluster), and intelligent technology journals (blue cluster), with the Journal of Robotic Surgery and IJCARS serving as central nodes that bridge interdisciplinary connections (Fig. 3E).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComprehensive Analysis of Top Journals (2015\u0026ndash;2025)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eJournal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePublications\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDegree_Centrality\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLast_Year\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJOURNAL OF ROBOTIC SURGERY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLECTURE NOTES IN COMPUTER SCIENCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCURRENT DIRECTIONS IN BIOMEDICAL ENGINEERING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTUDIES IN HEALTH TECHNOLOGY AND INFORMATICS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPPLIED ERGONOMICS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJOURNAL OF MEDICAL SYSTEMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJOURNAL OF UROLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUROLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPPLIED CLINICAL INFORMATICS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMJ OPEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMPUT INTELL NEUROSCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGYNECOLOGIC ONCOLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHUM FACTORS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJ ROBOT SURG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEDIATRIC ANESTHESIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLOS ONE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAMERICAN JOURNAL OF INFECTION CONTROL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAMERICAN JOURNAL OF SURGERY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eANESTHESIA AND ANALGESIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eANNALS OF SURGERY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCIN-COMPUTERS INFORMATICS NURSING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFRONT DIGIT HEALTH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIFMBE PROCEEDINGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINT J COMPUT ASSIST RADIOL SURG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINTERNATIONAL JOURNAL OF GYNECOLOGICAL CANCER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINTERNATIONAL JOURNAL OF MEDICAL ROBOTICS AND COMPUTER ASSISTED SURGERY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJAMIA OPEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Geographical Distribution and International Collaboration Patterns\u003c/h2\u003e\n \u003cp\u003eAccording to the literature analysis, the field of operating room intelligence demonstrates distinct geographical distribution characteristics and international collaboration patterns (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig. 4). The United States leads with 109 publications, followed by the United Kingdom (33), Germany (33), and China (28) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. 4A). VOSviewer-based international collaboration network analysis reveals a cooperative network encompassing 25 countries/regions with 7 clusters and a total link strength of 32 (Fig. 4B), where the United States occupies the core position (weight: 109, links: 14) as the most important international collaboration hub, while China serves as a major Asian participant (weight: 28, links: 4) primarily collaborating with the US, UK, Japan, and other countries. The global publication distribution heat map (Fig. 4C) further identifies three major research hotspots centered on North America (US), Europe (UK and Germany), and East Asia (China and Japan), with the network exhibiting a \u0026quot;core-periphery\u0026quot; structure characterized by geographical clustering, reflecting a regionalized collaboration model dominated by developed countries with gradually increasing participation from emerging economies.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComprehensive Country Analysis of Operating Room Intelligence Research (2015\u0026ndash;2025)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePublications\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDegree Centrality\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBetweenness Centrality\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGERMANY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUNITED KINGDOM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHINA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJAPAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINDIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUSTRALIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCANADA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFRANCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHINA(TAIWAN)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eITALY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSOUTH KOREA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFINLAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIRELAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUSTRIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNETHERLANDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSWEDEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSWITZERLAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTURKEY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRAZIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eISRAEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePOLAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSINGAPORE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBELGIUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDENMARK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSPAIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCZECH REPUBLIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGREECE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNORWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Institutional Distribution and Collaboration Patterns\u003c/h2\u003e\n \u003cp\u003eInstitutional analysis revealed that smart operating room research is predominantly concentrated in prestigious universities and medical institutions in Europe and North America (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig. 5A). Technical University of Munich had the highest publication output (7 articles), followed by Department of Surgery (6 articles), Purdue University (5 articles), and Duke University (4 articles). Among the top 20 productive institutions, most published 3 articles, reflecting the relatively dispersed nature of research in this field. From a temporal perspective, Department of Surgery demonstrated the longest research span (2016\u0026ndash;2025, 5 active years), while Technical University of Munich, despite having the highest publication count, showed a shorter research period (2022\u0026ndash;2024, 3 active years), indicating the emergence of new research hotspots in the field. Institutional collaboration network analysis revealed that 39 major institutions formed 14 collaborative clusters with 39 linkages and a total link strength of 58, primarily comprising clusters of US East Coast medical schools (red cluster), European technical universities (blue cluster), and clinical medical institutions (green cluster), with Technical University of Munich and Department of Surgery serving as key nodes connecting research forces across different regions and disciplines (Fig. 5B).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComprehensive Analysis of Top Institutions (2015\u0026ndash;2025)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eShort_Name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePublications\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDegree_Centrality\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eActive_Years\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTECHNICAL UNIVERSITY OF MUNICH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEPARTMENT OF SURGERY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePURDUE UNIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDUKE UNIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRIGHAM \u0026amp; WOMENS HOSP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAIRO UNIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCLEMSON UNIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEPT OBSTET \u0026amp; GYNECOL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEPT SURG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHANNOVER MEDICAL SCHOOL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHARVARD MED SCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINDIANA UNIV SCH MED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASSACHUSETTS GEN HOSP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAYO CLIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNINGBO UNIVERSITY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRUSH UNIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTOKYO WOMEN\u0026apos;S MEDICAL UNIVERSITY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUNIV LEEDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUNIV LEIPZIG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUNIV PITTSBURGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUNIVERSITY HOSPITAL RECHTS DER ISAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVANDERBILT UNIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOSPITAL UNIVERSITY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBAIRD INST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAIRO UNIVERSITY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCEDARS SINAI MED CTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHAN SCHOOL OF MEDICINE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHINA MED UNIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCLEMSON UNIVERSITY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOLUMBIA UNIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Author Productivity and Collaboration Network Analysis\u003c/h2\u003e\n \u003cp\u003eThe distribution of author publications in the field of operating room intelligence exhibits typical long-tail characteristics, conforming to Lotka\u0026apos;s Law in bibliometrics. According to author publication statistics (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), WILHELM D leads with 7 publications, establishing himself as the most influential scholar in this field; BERNHARD L follows closely with 6 publications; WAGNER L and XIANG W tie for third place with 5 publications each. Additionally (Fig. 6A). Lotka\u0026apos;s Law validation results show an author productivity distribution slope of -3.915, indicating that a small number of highly productive authors contribute a large volume of literature while most authors publish only 1\u0026ndash;2 articles (Fig. 6B). The author collaboration network analysis constructed using VOSviewer software reveals a network comprising 6 main clusters with a total link strength of 32, where WILHELM D serves as the network\u0026apos;s core node with 14 connections, making him the most important collaboration hub in this field (Fig. 6C). The collaboration network exhibits distinct clustering characteristics, with the blue cluster centered on WILHELM D including scholars such as BERNHARD L, WAGNER L, KOLB S, and JELL A, forming the most tightly connected research team. Other clusters are represented in different colors, including the red cluster (ABRAHAM J, MIGUCHI M, etc.) and the green cluster (BEN ABDALLAH A, KRONZER A, etc.), reflecting the diversified research team organizational patterns and relatively stable academic collaboration relationships in this field.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eProductivity and Network Centrality (2015\u0026ndash;2025)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePublications\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCitations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDegree Centrality\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWILHELM D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBERNHARD L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWAGNER L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXIANG W\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eABRAHAM J\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWACHS JP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZHOU T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCATCHPOLE K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAVUOTO L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGURU KA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHUSSEIN AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJELL A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKANNAMPALLIL T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKOLB S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLI Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMASAMUNE K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMURAGAKI Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNEUMUTH T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSTEFANIDIS D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYU D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAHMAD B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALVARADO N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eANDERSEN B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eANGER JT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAVIDAN MS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBANNON PG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBATES DW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBEN ABDALLAH A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBERLET M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBONIDIE M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e3.6 Keyword Analysis and Research Hotspot Evolution\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eKeyword analysis revealed core themes and developmental trends in smart operating room research (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, Fig. 7). Among 100 high-frequency keywords, \u0026quot;OPERATING ROOM\u0026quot; had the highest occurrence (100 times), followed by \u0026quot;ROBOTIC SURGERY\u0026quot; (89 times), \u0026quot;NURSING\u0026quot; (67 times), \u0026quot;TEAMWORK \u0026amp; COMMUNICATION\u0026quot; (44 times), and \u0026quot;ARTIFICIAL INTELLIGENCE\u0026quot; (40 times), reflecting the field\u0026apos;s emphasis on both technological applications and humanistic care (Fig. 7A). Regarding network centrality, \u0026quot;ROBOTIC SURGERY\u0026quot; demonstrated the highest degree centrality (0.863) and strength centrality (0.756), with \u0026quot;NURSING\u0026quot; (degree centrality 0.897, strength centrality 0.801) following closely, indicating these topics occupy central positions in the knowledge network. Keyword co-occurrence network analysis showed that 50 core keywords formed six major research clusters with 585 linkages and a total link strength of 1467, primarily focusing on operating room management, robotic technology, nursing practice, patient safety, quality improvement, and intelligent algorithms (Fig. 7B). Citation burst analysis identified 23 keywords with burst characteristics, with early hotspots including \u0026quot;operating room scheduling\u0026quot; (2017), and \u0026quot;multi-objective optimization\u0026quot; (2017), while recent emerging trends encompass \u0026quot;deep learning\u0026quot; (2020\u0026ndash;2023), \u0026quot;natural language processing\u0026quot; (2023), and \u0026quot;robotic scrub nurse\u0026quot; (2023\u0026ndash;2025) (Fig. 7C, 7D).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComprehensive Analysis of Top Keywords (2015\u0026ndash;2025)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKeyword\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDegree_Centrality\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStrength_Centrality\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOPERATING ROOM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROBOTIC SURGERY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNURSING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTEAMWORK \u0026amp; COMMUNICATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARTIFICIAL INTELLIGENCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSURGICAL INSTRUMENTS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAPAROSCOPIC SURGERY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.365\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROBOTICS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEFFICIENCY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePATIENT SAFETY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSURGEON\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*ROBOTIC SURGICAL PROCEDURES/METHODS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePROSPECTIVE STUDY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWORKFLOW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*ROBOTICS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eERGONOMICS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOPERATING-ROOM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOUTCOMES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePERFORMANCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSURGEONS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOMPLICATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDB - SCOPUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOPERATING ROOM PERSONNEL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePERIOPERATIVE NURSING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRANDOMIZED CONTROLLED TRIAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROBOTIC SCRUB NURSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSAFETY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIMULATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBODY MASS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCLINICAL ARTICLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDEEP LEARNING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEDUCATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOSPITALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIMPACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOPERATING ROOM NURSING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePERIOPERATIVE CARE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePROSTATECTOMY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQUALITY IMPROVEMENT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e*ROBOTIC SURGICAL PROCEDURES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCARE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDIAGNOSIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHUMAN FACTORS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSURVEYS AND QUESTIONNAIRES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTRANSPLANTATION (SURGICAL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALGORITHMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDECISION MAKING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGYNECOLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHYSTERECTOMY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIMPLEMENTATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLEARNING CURVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Thematic Evolution and Research Development Trends\u003c/h2\u003e\n \u003cp\u003eThematic evolution analysis identified six core research clusters in the smart operating room field: Surgical Robotics \u0026amp; Equipment, Nursing Staff \u0026amp; Scheduling, Surgical Settings \u0026amp; Workflow, Safety Quality \u0026amp; Risk Management, Intelligent Algorithms \u0026amp; Decision Support, Communication \u0026amp; Collaboration, and Information Systems \u0026amp; Data Management (Fig. 8A). The thematic relationship network revealed that Surgical Robotics \u0026amp; Equipment represents the largest cluster with the strongest inter-thematic connections, while Communication \u0026amp; Collaboration and Intelligent Algorithms \u0026amp; Decision Support developed relatively independently. Temporal evolution trends showed dynamic characteristics across 2015\u0026ndash;2025: Surgical Robotics \u0026amp; Equipment peaked in 2019 (33 occurrences), Surgical Settings \u0026amp; Workflow was most active in 2019 (28 occurrences), Nursing Staff \u0026amp; Scheduling experienced significant growth in 2019 (25 occurrences), while Intelligent Algorithms \u0026amp; Decision Support started strong in 2015 (10 occurrences) before stabilizing (Fig. 8B). Heat map analysis further revealed that Surgical Robotics \u0026amp; Equipment continuously intensified during 2016\u0026ndash;2019, Nursing Staff \u0026amp; Scheduling experienced concentrated emergence in 2018\u0026ndash;2019, and Safety Quality \u0026amp; Risk Management has gained increasing importance in recent years, reflecting the field\u0026apos;s evolution from technology-oriented towards human-machine collaboration and integrated quality-safety considerations (Fig. 8C, 8D).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eBy examining relevant literature, we conducted a comprehensive bibliometric analysis of smart OR nursing studies[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The bibliometric patterns observed in these studies provide insights into the opportunities and barriers facing the integration of smart technologies into OR nursing[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], suggesting key implementation challenges and identifying impediments that may prevent the translation of research findings into clinical practice.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Temporal Distribution and Journal Publication Patterns\u003c/h2\u003e\u003cp\u003ePublications grew from 16 (2015) to 49 (2024), with 2022\u0026ndash;2024 representing 45.4% of total output (132 out of 291 publications). The field demonstrated significant growth momentum, particularly with notable increases in 2018 (100% growth rate), 2022 (69.57% growth rate), and sustained growth in 2023\u0026ndash;2024 (12.82% and 11.36% respectively). Concurrently, 291 articles scattered across 210 journals following Bradford's Law, with International Journal of Computer Assisted Radiology and Surgery leading (9 publications) and Human Factors achieving highest impact (27.67 citations per article) against a field average of 3.67. The co-citation network reveals four interdisciplinary clusters bridged by Journal of Robotic Surgery and IJCARS.\u003c/p\u003e\u003cp\u003eThis temporal-journal evolution demonstrates a coherent pattern of field maturation from concentrated foundational work to diversified specialization. The inverse relationship between growing publication volume and declining citations directly corresponds to the wide journal scatter, where researchers transition from citing core foundational texts to drawing from increasingly diverse and specialized literature across multiple disciplinary venues[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The early research period (2015\u0026ndash;2017) established foundational knowledge in operating room automation, while the publication surge in 2022\u0026ndash;2024 reflects the field's maturation and practical implementation across diverse healthcare settings. The journal distribution pattern reinforces this evolutionary trajectory. Bradford's Law compliance with relatively even zone distribution indicates that operating room intelligence has not yet consolidated around dominant publication venues, reflecting the field's expansion across traditional disciplinary boundaries[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Human Factors'[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] exceptional citation performance (27.67) demonstrates that foundational human-centered design principles maintain broad relevance even as research diversifies, while the modest field average (3.67) suggests that specialized technical applications receive more limited cross-field recognition.\u003c/p\u003e\u003cp\u003eThe four-cluster co-citation structure (clinical medicine, computer informatics, intelligent technology) mirrors the temporal shift toward specialized applications, where researchers naturally gravitate toward domain-specific publication venues. While Journal of Robotic Surgery[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]and IJCARS[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] serve as crucial interdisciplinary bridges, this fragmentation across both time and publication landscape creates knowledge silos that challenge integrated field development. The temporal concentration of recent publications combined with journal dispersion suggests the field requires new mechanisms for knowledge synthesis to maintain coherent advancement despite increasing specialization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Geographic Distribution and Institutional Concentration Patterns\u003c/h2\u003e\u003cp\u003eThe dominance of developed countries\u0026mdash;particularly the US (109 publications), UK (33), and Germany (33)\u0026mdash;directly corresponds to institutional concentration among prestigious technical universities and medical centers, with Technical University of Munich (7 publications), Purdue University (5), and Duke University (4) leading research output. Meanwhile, emerging economies like China, despite substantial research capacity (28 publications), maintain more limited international connections (4 links) and correspondingly fewer leading institutions in global networks.This geographical-institutional convergence reflects critical infrastructure requirements that extend beyond research capacity alone. Leading countries benefit from established regulatory pathways like the FDA's (Food and Drug Administration) medical device approval framework and tight integration between elite academic institutions, world-class medical centers, and advanced technology companies[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The institutional collaboration network, organized around US East Coast medical schools, European technical universities, and clinical medical institutions, mirrors the geographical clustering pattern where regional proximity facilitates complex interdisciplinary partnerships essential for this field[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The temporal evolution reveals how institutional and geographical advantages reinforce each other. While established centers like Department of Surgery demonstrate sustained engagement (2016\u0026ndash;2025), new hotspots such as Technical University of Munich's intense recent activity (2022\u0026ndash;2024) reflect expanding but still concentrated research infrastructure. This pattern suggests that specialized resources\u0026mdash;advanced robotics laboratories, clinical testing environments, regulatory expertise, and substantial capital investment\u0026mdash;remain concentrated in elite institutions within developed countries, creating barriers for emerging economies to achieve full integration into global research networks[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, even these resource-rich geographical regions and institutions face substantial implementation barriers. The relatively dispersed publication pattern among institutions (most contributing only 3 articles) indicates that even well-funded centers struggle to maintain sustained research programs due to high capital costs, complex clinical workflows, and surgical team resistance to automation[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This suggests that successful operating room intelligence development requires not just technological innovation but fundamental healthcare system transformation\u0026mdash;a challenge that transcends individual institutional or national capabilities.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Author Productivity and Collaboration Network Patterns\u003c/h2\u003e\u003cp\u003eThe author productivity analysis reveals a highly concentrated landscape with WILHELM D leading at 7 publications and serving as the central collaboration hub (14 connections), followed by a small core group including BERNHARD L, WAGNER L, and XIANG W. The Lotka's Law validation (slope = -3.915) confirms extreme concentration where few authors drive knowledge creation while most contribute only 1\u0026ndash;2 articles. The collaboration network shows 6 distinct clusters with WILHELM D functioning as the primary knowledge broker. This steep concentration reflects the field's specialized nature, requiring substantial expertise and resources that few researchers can sustain consistently. The pattern indicates operating room intelligence research remains in an early consolidation phase, where pioneer researchers establish foundational knowledge and collaborative frameworks. WILHELM D's central position enables cross-cluster knowledge transfer but creates potential bottlenecks for field advancement[\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The distinct clustering patterns suggest research teams organize around complementary expertise rather than geographical proximity, forming stable collaborative relationships essential for complex interdisciplinary projects. However, this heavy dependence on individual researchers poses both advantages\u0026mdash;enabling deep specialization and sustained collaboration\u0026mdash;and risks for field development if key contributors become unavailable.\u003c/p\u003e\u003cp\u003e\u003cb\u003e4.4 Keyword Analysis and Thematic Evolution Patterns\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong 100 high-frequency keywords, \"OPERATING ROOM\" leads with 100 occurrences, followed by \"ROBOTIC SURGERY\" (89), \"NURSING\" (67), \"TEAMWORK \u0026amp; COMMUNICATION\" (44), and \"ARTIFICIAL INTELLIGENCE\" (40). Regarding network centrality, \"NURSING\" demonstrates the highest degree centrality (0.897) and strength centrality (0.801), with \"ROBOTIC SURGERY\" following closely (degree centrality 0.863, strength centrality 0.756), indicating these topics occupy central positions in the research landscape.\u003c/p\u003e\u003cp\u003eKeyword co-occurrence network analysis revealed that 50 core keywords formed six major research clusters with 585 linkages and a total link strength of 1467, primarily focusing on operating room management, robotic technology, nursing practice, patient safety, quality improvement, and intelligent algorithms. Citation burst analysis identified 23 keywords with burst characteristics, revealing temporal evolution from early focus on \"operating room scheduling\" (2017) and \"multi-objective optimization\" (2017) to recent emphasis on \"deep learning\" (2020\u0026ndash;2023), \"natural language processing\" (2023), and \"robotic scrub nurse\" (2023\u0026ndash;2025).\u003c/p\u003e\u003cp\u003eThis evolution demonstrates the field's progression through distinct phases: from operating room scheduling and optimization approaches to AI integration, and from technology-centered approaches to human-machine collaboration systems. The high centrality of both \"ROBOTIC SURGERY\" and \"NURSING\" reflects the field's recognition that successful automation requires deep clinical workflow integration rather than purely technological solutions[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The citation burst progression from scheduling optimization to sophisticated AI applications demonstrates accelerating technological adoption, while the emphasis on teamwork, communication, and quality improvement indicates field maturation toward comprehensive human-machine collaborative systems[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Future Research Directions and Policy Implications\u003c/h2\u003e\u003cp\u003eFuture research must prioritize comparative effectiveness studies evaluating intelligent technologies' impact on patient outcomes and operational efficiency across diverse healthcare settings. Cross-national validation models should assess technology feasibility while accounting for infrastructure and resource variations. Implementation science methodologies must examine organizational factors affecting adoption, including culture, leadership, and workflow redesign, alongside health equity assessments to prevent exacerbating healthcare disparities[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].Global regulatory harmonization is critical for broader implementation. Current frameworks inadequately address AI systems that continuously learn and evolve, requiring new approaches balancing innovation with patient safety. Evidence standards must encompass dynamic AI performance beyond traditional device metrics, while professional scope guidelines should clarify roles in AI-augmented environments and liability frameworks address legal concerns impeding adoption[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Meanwhile, enhancing collaboration between researchers, clinicians, policymakers, and technology developers is essential. International partnerships should explore culturally appropriate strategies for resource-limited countries, ensuring global healthcare equity. Interdisciplinary collaborations must integrate technical, economic, and organizational expertise, while industry-academia partnerships should develop scalable implementation models adaptable across diverse healthcare contexts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Study Limitations\u003c/h2\u003e\u003cp\u003eThis analysis has several limitations affecting implementation planning implications. Academic literature may exhibit publication bias toward positive results, potentially overrepresenting successful implementations while underreporting failed pilots. The focus on English-language publications from developed countries may miss important implementation experiences from diverse healthcare contexts.\u003c/p\u003e\u003cp\u003eAdditionally, temporal lag in academic publishing means current implementation challenges may not be reflected in peer-reviewed literature, potentially limiting findings' relevance for immediate decision-making.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eThis study explores operating room intelligence research through comprehensive bibliometric analysis, providing an overview of its evolution, identifying emerging trends, and evaluating its progression from 2015\u0026ndash;2025.\u003c/p\u003e\u003cp\u003eKey research hotspots include robotic surgery, nursing automation, AI integration, and human-machine collaboration systems. The field has evolved from operational optimization (2017) to deep learning applications (2020\u0026ndash;2023) and robotic scrub nurse development (2023\u0026ndash;2025). Future research should focus on comparative effectiveness studies, cross-national validation models, and implementation science methodologies addressing organizational factors and health equity concerns.\u003c/p\u003e\u003cp\u003eInternational collaboration reveals significant concentration, with the United States leading (109 publications) alongside the UK, Germany, and China. However, emerging economies maintain limited international connections, highlighting global research inequities. Enhanced collaboration between researchers, clinicians, policymakers, and technology developers is essential for translating technological advancements into practical clinical applications. The field faces substantial implementation barriers despite technological progress. High capital costs, complex clinical workflows, and resistance to automation challenge even well-resourced institutions. Moving forward, addressing regulatory harmonization for AI systems, developing evidence standards for dynamic AI performance, and establishing professional scope guidelines will be crucial for broader adoption.\u003c/p\u003e\u003cp\u003eFuture research should prioritize global health equity, ensuring operating room intelligence benefits diverse healthcare contexts rather than exacerbating existing disparities. Interdisciplinary partnerships must integrate technical, economic, and organizational expertise while developing scalable implementation models. The field requires enhanced knowledge synthesis mechanisms to prevent disciplinary fragmentation and maintain coherent advancement toward comprehensive human-machine collaborative systems that improve patient outcomes and healthcare delivery worldwide.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eWoSCC: Web of Science Core Collection\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData materials are real and available. Yaling Wang should be contacted if someone wants to request the data from this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThank you Alain Vandal (Biostatistics \u0026amp; Epidemiology, Auckland University of Technology) for the introduction to RStudio\u0026trade;\u0026nbsp;and Andrew (Drew) South (Liaison Research Librarian, Auckland University of Technology) for search and Endnote\u0026trade;\u0026nbsp;support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003ePresent Address: Department of Nursing, Melbourne School of Health Sciences, 161 Barry St, Carlton, Victoria, 3053, Australia\u003c/p\u003e\n\u003cp\u003eRebecca J. Jarden\u003c/p\u003e\n\u003cp\u003eSchool of Clinical Sciences, Auckland University of Technology (AUT), North Shore Campus, 90 Akoranga Drive, Northcote, Auckland, 0627, New Zealand\u003c/p\u003e\n\u003cp\u003eRebecca J. Jarden, Margaret Sandham \u0026amp; Jane Koziol-McLain\u003c/p\u003e\n\u003cp\u003eSchool of Engineering, Computing and Mathematical Sciences (D-75), Auckland University of Technology, AUT Tower, 2-14 Wakefield Street, Auckland, 1010, New Zealand\u003c/p\u003e\n\u003cp\u003eAjit Narayanan\u003c/p\u003e\n\u003cp\u003eSchool of Clinical Sciences and School of Public Health and Psychosocial Studies, Auckland University of Technology (AUT), North Shore Campus, 90 Akoranga Drive, Northcote, Auckland, 0627, New Zealand\u003c/p\u003e\n\u003cp\u003eRichard J. Siegert\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first author RJ has contributed to this research inception, drafting and editing the manuscript in its entirety. All other authors AN, MS, RS, JKM have contributed to project conception, revising, editing and approving the manuscript in its entirety. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Rebecca J. Jarden.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s Note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRights and permissions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOpen Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eManickam P, Mariappan SA, Murugesan SM, Hansda S, Kaushik A, Shinde R, Thipperudraswamy SP (2022) Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare. Biosens (Basel) 12(8)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTehranineshat B, Rakhshan M, Torabizadeh C, Fararouei M (2019) Compassionate Care in Healthcare Systems: A Systematic Review. J Natl Med Assoc 111(5):546\u0026ndash;554\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eErgin E, Karaarslan D, Şahan S, Bing\u0026ouml;l \u0026Uuml; (2023) Can artificial intelligence and robotic nurses replace operating room nurses? The quasi-experimental research. 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How big data and AI are transforming healthcare: Challenges and opportunities. S Afr Med J 114(1):22\u0026ndash;26\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Operating room intelligence, Intelligent nursing, Robotic surgery, Artificial intelligence, Bibliometric analysis, Smart healthcare, Perioperative care, Human-machine collaboration","lastPublishedDoi":"10.21203/rs.3.rs-7157116/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7157116/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Objectives: \u003c/strong\u003eThe integration of intelligent technologies in operating room nursing represents a rapidly evolving field requiring systematic analysis to understand global research trends and development patterns. This study aimed to comprehensively analyze worldwide research trends, identify key contributors, and reveal emerging developments in intelligent operating room nursing through bibliometric analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A comprehensive literature search was conducted across six international databases (Web of Science, Scopus, PubMed, Embase, Cochrane Library, and CINAHL) from January 2015 to April 2025. Following rigorous screening criteria, 291 eligible articles were analyzed using R software with bibliometrix package, VOSviewer, and CiteSpace for bibliometric analysis, network visualization, and thematic evolution assessment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Publication output increased from 16 articles in 2015 to 49 in 2024, with 45.4% of total publications concentrated in 2022-2024. The United States dominated with 132 publications, followed by Germany and the United Kingdom (33 each). Technical University of Munich led institutional contributions with 7 publications. Six major research themes emerged: operating room management, robotic technology, nursing practice, patient safety, quality improvement, and intelligent algorithms. Keyword analysis revealed evolution from early focus on operating room scheduling and multi-objective optimization (2017) to deep learning applications (2020-2023), natural language processing (2023), and robotic scrub nurse development (2023-2025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe field demonstrates rapid growth with clear thematic evolution toward human-machine collaboration systems. Future research should prioritize comparative effectiveness studies, implementation science methodologies, and global health equity considerations to ensure widespread clinical translation.\u003c/p\u003e","manuscriptTitle":"Global Research Trends, Hotspots, Impacts, and Emerging Developments in Intelligent Operating Room Nursing: A 10-Year Bibliometric Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-21 09:42:24","doi":"10.21203/rs.3.rs-7157116/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f743551c-0a64-4546-b5e4-1ae0450ef25c","owner":[],"postedDate":"July 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51747977,"name":"Nursing"},{"id":51747978,"name":"General Surgery"}],"tags":[],"updatedAt":"2025-07-21T09:42:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-21 09:42:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7157116","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7157116","identity":"rs-7157116","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}