Keyword Co-occurrence Network Research Articles

Effects of anti-epidemic (quarantine) measures on people during the COVID-19 pandemic: applying social network analysis to identify the key topics

The aim of this study was to examine the public reaction to the implementation of quarantine measures through a personality-oriented discourse.Materials and methods. Text data were collected from a microblogging platform, resulting in a dataset of 86,750 texts related to the topics of “pandemic” and “quarantine measures”. The lexical conceptualization of the pandemic and quarantine measures represented in the texts was analyzed through the lens of a personality-oriented discourse. Text lemmatization was conducted using the “snowball” library. A data feature matrix was then created based on the lemmatized tokens, which included 53 tokens with a frequency of use exceeding 1,300 times. The Social Network Analysis (SNA) method was used to create a keyword co-occurrence network consisting of undirected graphs. This analysis was performed using the free software R version 4.4.1, with the assistance of the Quanteda library, built-in “base” packages, and the gsub function.Results. The resulting network consisted of 53 key lexemes, which actors used to respond to quarantine measures in the personality-oriented discourse. The central node of the network was “coronavirus”, which was used 79,838 times between March 1 and April 30, 2020. The nearest nodes were “test” (used 4,663 times) and “Russia” (used 5,848 times). This network had high centrality, indicating that despite strict restrictive measures, the focus of the general public was on the pandemic itself and its impact on society rather than on the restrictions imposed.Conclusion. The implementation of these anti-epidemic measures has created a unique sociolinguistic world view, reflecting the interaction between society and the outside world in a time of uncertainty and health risks, affecting the analysis of information and the behavioral strategies chosen by society.

Mapping the Research on Healthcare Delivery in Business and Management in the Last Two Decades (2002–2021): A Bibliometric Network Analysis

Healthcare delivery is an evolving field of research concerned with promoting good health and well-being for all. To comprehend the progress of the research field, 909 healthcare delivery records were systematically collected from 20 categories aligned to business and management in the Web of Science database, published between 2002 and 2021. Later, the articles were analysed using the Bibliometrix tool to extract information on the annual number of publications, the records of highest publications, keyword co-occurrence networks and thematic evolution during the study period. The findings showed that the United States is the country with the highest contribution to the field, and the University of Washington is the institution with the highest contribution. Additionally, 10 significant clusters of the published articles were identified. The thematic evolution shows that the focus is now more on patient outcomes and the adoption of virtual health delivery models. This study is one of the first to holistically review the healthcare delivery research landscape of the past two decades to identify relevant themes from a multidisciplinary perspective, guiding researchers to pursue novel research studies.

Scientometric Analysis of Personalized Learning Research

This study presents a scientometric analysis of personalized learning research from 2020 to 2024. Using the Lens database, 4,463 scholarly articles were analyzed to identify key trends and patterns in this rapidly evolving field. The analysis revealed consistent publication growth over the 5 years, with journal articles dominating as the primary publication type. Hessian Normal University emerged as the most productive institution, while Jackson Steinher was identified as the most prolific author. The United States and China were the leading countries in terms of research output. Education and Information Technologies was the top journal publishing personalized learning research. Co-authorship network analysis highlighted collaborative patterns among researchers, while keyword co-occurrence networks revealed the centrality of artificial intelligence and related concepts in the field. Citation analysis identified influential documents and sources shaping the discourse. The findings suggest an increasing focus on integrating AI and machine learning into personalized learning systems and a growing emphasis on interdisciplinary approaches. This scientometric overview provides valuable insights into the current state and emerging trends in personalized learning research, which may inform future studies and applications in this domain.

Emotional analysis of operating room nurses in acute care hospitals in Japan: insights using ChatGPT

AimThis study aimed to explore the emotions of operating room nurses in Japan towards perioperative nursing using generative AI and human analysis, and to identify factors contributing to burnout and turnover.MethodsA single-center cross-sectional study was conducted from February 2023 to February 2024, involving semi-structured interviews with 10 operating room nurses from a national hospital in Japan. Interview transcripts were analyzed using generative AI (ChatGPT-4o) and human researchers for thematic, emotional, and subjectivity analysis. A comparison between AI and human analysis was performed, and data visualization techniques, including keyword co-occurrence networks and cluster analysis, were employed to identify patterns and relationships.ResultsKey themes such as patient care, surgical safety, and nursing skills were identified through thematic analysis. Emotional analysis revealed a range of tones, with AI providing an efficient overview and human researchers capturing nuanced emotional insights. High subjectivity scores indicated deeply personal reflections. Keyword co-occurrence networks and cluster analysis highlighted connections between themes and distinct emotional experiences.ConclusionsCombining generative AI with human expertise offered nuanced insights into the emotions of operating room nurses. The findings emphasize the importance of emotional support, effective communication, and safety protocols in improving nurse well-being and job satisfaction. This hybrid approach can help address emotional challenges, reduce burnout, and enhance retention rates. Future research with larger and more diverse samples is needed to validate these findings and explore the broader applications of AI in healthcare.

Trends in urban edible landscapes: a comprehensive bibliometric analysis

Abstract In response to the dual challenges of urbanization and population growth, urban edible landscapes have emerged as innovative green infrastructure with the potential to address ecological, social, economic, and food security issues. This study systematically analyzes 3,089 publications from 2000 to 2024, sourced from the Web of Science database, using keywords such as ‘Edible Landscapes,’ ‘Edible Garden,’ ‘Rooftop Garden,’ ‘Community Garden,’ and ‘Productive Landscape.’ This study adopts bibliometric and visualization tools, such as CiteSpace and R, to systematically sort out and visualize the research hotspots, knowledge structure, emerging trends, and the evolution of this field. The analysis begins with a co-occurrence network of keywords, identifying core topics and evolving research trends. Cluster analysis further delves into various research themes, elucidating the field’s main directions and focal points. The paper highlights the multifaceted impacts of edible landscapes on urban ecosystems and residents’ well-being, emphasizing their critical role in promoting urban sustainability. Additionally, it outlines strategic recommendations for policymakers and stakeholders to enhance the implementation and expansion of urban edible landscapes in alignment with sustainable development goals. This research provides comprehensive insights into the historical evolution, status, and future trends of urban edible landscape studies, offering valuable guidance for policy development and practical innovation in both academic and real-world contexts.

Exploring trends and topics in hybrid intelligence using keyword co-occurrence networks and topic modelling

Exploring trends and topics in hybrid intelligence using keyword co-occurrence networks and topic modelling

Science mapping the literature in Applied Linguistics secondary research: navigating knowledge evolution from an epistemic perspective (1970–2022)

Applied Linguistics (AL) research has undergone significant transformation in recent decades, accompanied by a surge in secondary research within the field. Using WoS data, we conducted an extensive bibliometric analysis of 881 secondary research articles and 44,752 references spanning 1970 to 2022. Utilizing CiteSpace's “author co-citation network,” “keyword co-occurrence network,” and “burst detection” analyses, we identified core research clusters, their key topics, connections, and significant bursts. Then we synthesize all the main clusters' content through in-depth analysis. Our findings reveal the growing popularity of secondary research in AL, with substantial contributions from Asian countries despite Anglo-American dominance. Key disciplines influencing the AL's intellectual structure include Psychology, Neurology, Sociology, Education, and Computer Science. Four thematic strands anchor AL's conceptual structure: Language Education & Computer Science, Psycholinguistics, Neurolinguistics, and Sociolinguistics. The top-ranked co-cited authors by burst strength are Moher, Chomsky, and Botting. Using a data-driven theory of scientific revolution, we identify emerging research trends and track dynamic changes from an epistemic perspective. This dynamic perspective contributes to a more nuanced understanding of the evolution of the AL domain, signaling a new era of Critical Applied Linguistics. This study, an empirically-based overview of AL secondary research, offers important implications for future research.

Eğitim Alanında Örgütsel Vatandaşlık Davranışı Literatürünün Bibliyometrik Analizi

This study was conducted using the bibliometric analysis method to examine the structural and intellectual composition of publications themed on organizational citizenship behavior (OCB) in the field of educational research. Articles published between 2000 and 2024 were evaluated based on searches in the Web of Science database. Bibliometric indicators such as publication count, citation distributions, authors, countries, institutions, keywords, and journals were analyzed in the study. Using the Biblioshiny software, keyword co-occurrence networks and thematic maps were created, and development trends in the field were detailed. The results indicate that studies on OCB have increased since 2000, with a notable intensification after 2015. However, despite the rise in publication numbers, a decline in citations per article has been observed. This trend has been associated with theoretical and empirical saturation in the literature. The themes most frequently associated with OCB include job satisfaction, leadership, commitment, organizational culture, and motivation. The most influential authors and studies in the OCB literature were identified, with Somech A. and their works standing out in particular. Additionally, the journals with the highest number of publications included Journal of Educational Administration and Educational Management Administration & Leadership. This study serves as a significant guide for those researching OCB, offering insights into trends and connections in the literature while pointing to new research opportunities in the field. Keywords: Organizational citizenship behavior, bibliometrics, scientific mapping

BIBLIOMETRIC ANALYSIS OF RESEARCH ON NON-DESTRUCTIVE TESTING IN AEROSPACE

This study presents a bibliometric analysis of non-destructive testing (NDT) in aerospace, emphasising trends, collaborations, and advancements. Using Scopus data and VOSviewer software, 1,385 publications spanning 2014 to 2025 were analysed. The methodology included keyword co-occurrence, citation analysis, and collaborative network visualisation to identify influential authors, emerging topics, and interdisciplinary connections. Results indicate a steady increase in publications, peaking in 2024, reflecting sustained global interest and advancements in aerospace NDT. Dominant keywords include "non-destructive testing," "structural health monitoring," and "composites," highlighting a primary focus on structural integrity and material evaluation. Recent trends show the integration of artificial intelligence, evidenced by the rise of terms such as "machine learning" and "deep learning," signalling a shift toward digitalised, intelligent NDT systems. Collaboration analysis revealed distinct author clusters and international partnerships, with significant contributions from China, the United States (US), and the United Kingdom (UK), supported by major funding organisations. Integrating engineering, materials science, and emerging technologies like infrared thermography and additive manufacturing underscores the field’s interdisciplinary nature. Findings also reveal an evolving focus on lightweight composite materials and advanced defect detection methods, essential for aerospace safety and efficiency. This study concludes that bibliometric analysis is a valuable tool for identifying research gaps, fostering interdisciplinary collaboration, and guiding future innovations in aerospace NDT.

Is the Swallow Tail Sign a Useful Imaging Biomarker in Clinical Neurology? A Systematic Review.

Loss of dorsolateral nigral hyperintensity (DNH) in iron-sensitive sequences of Magnetic Resonance Imaging (MRI), also described as "swallow tail sign" (STS) loss, has shown promising diagnostic value in Parkinson's Disease (PD) and Atypical Parkinsonian Syndromes (APS). To conduct a bibliometric analysis on substantia nigra MRI and a systematic review on the clinical utility of STS visual assessment on Susceptibility-Weighted Imaging in various clinical entities. VOSviewer's keyword co-occurrence network was employed using Web of Science (WOS). Complying with the PRISMA statement, we searched MEDLINE, WOS, SCOPUS, ProQuest and Google Scholar for peer-reviewed studies conducted in vivo, excluding quantitative imaging techniques. DNH is a relatively novel parameter in substantia nigra MRI literature. Our SWI-focused review included 42 studies (3281 patients). Diagnostic accuracy of STS loss for PD/APS differentiation from controls and for Lewy Body Dementia differentiation from other dementias was 47.8-98.5% and 76-90%, respectively, with poorer capacity, however, in delineating PD from APS. STS evaluation in idiopathic REM sleep behavior disorder, a sign of prodromal PD, was typically concordant with nuclear scans, identifying subjects with high conversion risk. Iron deposition can affect STS in Multiple Sclerosis and STS loss in Amyotrophic Lateral Sclerosis is linked with multisystem degeneration, with poorer prognosis. In healthy individuals iron-induced microvessel changes are suspected for false positive results. STS assessment exhibits potential in different settings, with a possibly intermediate role in the diagnostic work-up of various conditions. Its clinical utility should be explored further, through standardized MRI protocols on larger cohorts.

Extracapsular Dissection Versus Superficial Parotidectomy for Benign Parotid Tumors: A Bibliometric and Visual Analysis.

Utilizing data extracted from Scopus and Web of Science databases, all the publications comparing "Superficial Parotidectomy" AND "Extracapsular dissection" were considered. Original research, review articles, letters, and note articles were included. Data about publication frequency, language distribution, authorship patterns, co-occurrence of keywords, and citation counts were extracted. In addition, visual analysis techniques such as co-authorship, keyword co-occurrence and citation networks, and bibliographic coupling maps were employed to visualize relationships and patterns within the literature. From the Scopus database, a total of 152 records were identified and screened, resulting in the inclusion of 42 papers. The most cited paper (102 times) was published in Laryngoscope Journal. Bibliometric analysis of co-citation revealed 797 cited references and 1284 cited authors. From the Web of Science database, a total of 211 records were identified and screened, resulting in the inclusion of 28 papers. This study provides a deeper overview of the landscape surrounding the comparison between extracapsular dissection and superficial parotidectomy for benign parotid tumors and insights about research trends, collaboration patterns, and knowledge dissemination in this clinical area to facilitate evidence-based practice and guide future research.

Research trends and hotspots on septic shock: a bibliometric study and visualization analysis.

Septic shock, the most severe stage of sepsis, causes potential circulatory failure and abnormal cell metabolism which are severe enough to affect prognosis, increase mortality, and impose significant burdens on the medical system. Despite a growing number of studies exploring the pathophysiology, epidemiology, and risk factors, research trends and hotspots in septic shock remain lacking. This study aims to create a visual knowledge map, identify research hotspots, and predict prospective trends based on bibliometric analysis. We searched for publications related to septic shock in Web of Science Core Collection up to June 15, 2023. CiteSpace5.5 R2, VOS viewer and Pathfinder were used to evaluate the annual publications, countries, institutions, journals and keywords. We also analyzed the collaboration among countries, institutions and authors, and identified research hotspots and frontiers. A total of 4,208 English papers were included in the analysis, and the annual publication displayed a slow upward trend. In terms of publication volume, the top three countries were the United States, France, and Germany, and the University of Pittsburgh (the United States) ranked first (n = 85) among all institutions, with Jeanlouis Vincent from Erasmus Medical Center (Netherlands) as the most published author (n = 32). According to the collaborative network, the United States had the highest level of cooperation, and the University of Pittsburgh, the University of Toronto, and Columbia University were the institutions with the most foreign cooperation. Additionally, the co-author network revealed that scholars such as Jeanlouis Vincent, Rinaldo Bellomo, and Djillali Annane, had the strongest collaborations. The co-citation network showed that the top 3 most cited articles were: Singer M (2016), Rhodes A (2017), Dellinger RP (2013), and the top 3 most cited journals were Crit Care Med (3,664 times), N Engl J Med (3,207 times), Intens Care Med (3,096 times) in this field. In the keyword co-occurrence network, the most frequent keywords were "septic shock" (2531), "sepsis" (1667), and "mortality" (569), indicating the current research hotspots. Pathobiology, fluid therapy, and endotoxic septic shock were emerging trends in research. By using bibliometrics, this study reviewed the studies in septic shock and revealed the hotspots and cutting-edge trends, including the pathogenesis of complications, the development of new biomarkers, the timing and methods of alternative treatments, and the rehabilitation trajectory, etc., which provided a reference for subsequent studies in septic shock.

Examining AI Use in Educational Contexts: A Scoping Meta-Review and Bibliometric Analysis

AbstractWith growing research attention in artificial intelligence in education (AIED), there is a profusion of systematic reviews that have investigated AI integration in a wide variety of educational contexts, including PreK-12 schools and higher education. Even though existing systematic reviews have explored effects of AI on education, few studies have synthesized the results of those reviews. To fill this gap, we conducted a scoping meta-review and bibliometric analysis to map the broad field of AIED and identify research gaps based on our analysis. Following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, we searched Scopus and Web of Science and collected 126 review articles from 2014 to Mid-August of 2023 that satisfied our inclusion criteria. Specifically, we used four criteria for the screening process: (1) the article’s full text was available in English; (2) the article was published in peer-reviewed journals; (3) the article was a systematic review; and (4) the article was focused on AI use in one or multiple educational context(s). Our findings revealed the key characteristics of the included reviews, such as keyword co-occurrence network, co-authorship network, geographic distribution of authorship, educational levels, and subject areas. Three major themes related to AIED were generated, encompassing AI’s impact on teaching, learning, and administration. We also identified commonalities and nuanced differences between PreK-12 education and higher education along with research areas that have been underexplored. Our study not only expanded understanding of studies on AIED, but also provided suggestions on how to strengthen future research.

Research Progress and Hotspots Analysis of Apoplastic Barriers in the Roots of Plants Based on Bibliometrics from 2003 to 2023.

The apoplastic barriers, composed of Casparian strip (CS) and suberin lamellae (SL), are integral to the regulation of water and plant nutrient uptake in plants, as well as their resilience to abiotic stresses. This study systematically examines the research developments and emerging trends in this field from 2003 to 2023, utilizing bibliometric tools such as Web of Science, CiteSpace, and VOSviewer to analyze a dataset of 642 publications. This paper reviews the cooperation of different countries, institutions, and scholars in apoplastic barriers research based on cooperative network analysis. In the field, China has the highest number of publications, the University of Bolton has the highest number of publications, and Niko Geldner is the author with the maximum number of publications. Notably, 27 publications were identified as highly cited, with their research primarily focusing on (1) genes, proteins, enzymes, and hormones regulating the formation of apoplastic barriers; (2) the influence of adversity stress on apoplastic barriers; (3) the chemical components of apoplastic barriers; (4) the evaluations of research progress on apoplastic barriers. Combined with the keyword co-occurrence network diagram, it is proposed that future research directions in this field should be as follows: (1) physiological functions of apoplastic barriers in plant root; (2) differences in the formation of apoplastic barriers with different root systems; (3) methods to promote apoplastic barriers formation; and (4) application of molecular biology techniques. The present study provides a further understanding of the trends in apoplastic barriers, and the data analyzed can be used as a guide for future research directions.

Mapping the relationship between alcohol use disorder and gut microbiota: a 20-year bibliometric study.

Alcohol use disorder (AUD) is a psychiatric disorder that is widespread worldwide. Alcohol use is a significant contributor to the global burden of death, disability and disease. Modulation of the gut microbiota is a promising approach to improve the efficacy and minimize the adverse effects of colorectal cancer treatment. The relationship between the presence of microbes and AUD has been widely validated. However, few studies have examined this relationship using bibliometric methods. Therefore, this study analyzes the research hotspots and trends in human gut microbiology and AUD over the last two decades from a bibliometric perspective. This study aims at provide new directions for basic and clinical research in this field. A comprehensive discussion of the relationship between the current state of research and trends in AUD and intestinal flora. We collected publications from the Web of Science Core Collection database from 2003 to 2023 according to established inclusion criteria. We analyzed countries, institutions, authors, and research contributions using CiteSpace, VOSviewer, and Scimago Graphics to visualize research trends in the field. A total of 2,102 publications were obtained, with a rapid increase in the number of publications since 2016. The United States and China are major contributors to the field and have established a network of partners in several countries. Five hundred ninety-five academic journals published articles on the topic. The author with the highest number of publications is Prof. Bernd Schnabl of the Department of Gastroenterology at the University of California, San Diego. In addition to "gut flora" and "AUD," high frequency words in the keyword co-occurrence network analysis included alcoholic liver disease, tryptophan metabolism, enterohepatic axis, and fecal microbial transplantation. The results of this study provide a bibliometric analysis and visualization of key research areas in the gut microbiota and AUD over the past 20 years. The results suggest that the role of the gut microbiota in AUD and its potential mechanisms, especially therapeutic targets, should be closely monitored and could become a hot topic in the field.

A Latent Space Model for Weighted Keyword Co-Occurrence Networks with Applications in Knowledge Discovery in Statistics

Keywords are widely recognized as pivotal in conveying the central idea of academic articles. In this article, we construct a weighted and dynamic keyword co-occurrence network and propose a latent space model for analyzing it. Our model has two special characteristics. First, it is applicable to weighted networks; however, most previous models were primarily designed for unweighted networks. Simply replacing the frequency of keyword co-occurrence with binary values would result in a significant loss of information. Second, our model can handle the situation where network nodes evolve over time, and assess the effect of new nodes on network connectivity. We use the projected gradient descent algorithm to estimate the latent positions and establish the theoretical properties of the estimators. In the real data application, we study the keyword co-occurrence network within the field of statistics. We identify popular keywords over the whole period as well as within each time period. For keyword pairs, our model provides a new way to assess the association between them. Finally, we observe that the interest of statisticians in emerging research areas has gradually grown in recent years. Supplementary materials for this article are available online.

Do ESG Practices Promote Financial Performance? Comparison of English, Chinese, and Korean Papers Through Bibliometric and Meta-Analysis

Companies engaged in ESG practices to enhance financial performance and demonstrate a commitment to sustainable development. However, the relationship between ESG and financial performance remained unclear, as prior studies were replete with contradictory paradoxes. This study aimed to comprehensively analyze the evolution of ESG practices and their impact on financial performance by comparing English, Chinese, and Korean papers. This study employed a mixed-methods approach and conducted a bibliometric analysis on 2659 ESG-related papers to unveil temporal trends and conduct a meta-analysis. Stratifying by language, linguistic and cultural nuances influencing the relationship were identified. The bibliometric analysis revealed a significant turning point in ESG research around 2019, marked by a surge in publications across languages. Keyword co-occurrence networks highlighted distinct focal points, with financial performance appearing in all the research themes. The meta-analysis indicated a low overall positive correlation between ESG and financial performance. However, Chinese and Korean papers demonstrated a moderate correlation, while English papers showed no significant correlation. This study informed scholarly, managerial, and policy discourse on sustainable business practices, contributing to the evolving ESG landscape.

Mindfulness at Work: A Bibliometric Analysis of Key Authors, Themes, and Trends

Background: Mindfulness in the workplace has gained significant attention for its potential to enhance well-being, reduce stress, and improve productivity. As interest in workplace mindfulness has grown, a need arises to systematically analyze the influential studies, key authors, themes, and trends shaping this field. Objectives: This study aims to map the intellectual landscape of "mindfulness at work" through a bibliometric analysis, identifying key contributions, collaboration networks, thematic clusters, and publication trends over time. Methods: Utilizing data from Lens.org, we conducted a bibliometric analysis using VOSviewer and WordSift tools to examine co-authorship, keyword co-occurrence, and citation networks, as well as keyword frequency and thematic trends. Findings: The analysis identifies central figures such as Jochen Reb and Christopher J. Lyddy and highlights prominent themes including well-being, stress management, and engagement, with a peak in publication interest around 2019. The study also reveals isolated research clusters, suggesting limited interdisciplinary integration within the field. Conclusion: While mindfulness research at work is growing, opportunities exist for deeper cross-disciplinary collaboration and broader integration of themes. Novelty: This study provides a comprehensive overview of the field’s development, offering insights into emerging trends, key influencers, and areas for future exploration, making it a valuable resource for researchers, practitioners, and policy-makers interested in advancing workplace mindfulness.

A bibliometric analysis of research on physical activity and fitness among preschool children in Asia (2020-2024)

Background: Physical activity (PA) and physical fitness (PF) are essential for preschool children's development. However, rising childhood obesity and sedentary lifestyles are concerning. A comprehensive analysis of the research landscape is necessary to inform better interventions and policies. Research Objectives: This study aims to conduct a bibliometric analysis of research on physical activity and physical fitness among preschool children in Asia from 2020 to 2024. Methods: The analysis was based on publications from Scopus and Web of Science, focussing on keyword co-occurrence, citation analysis, and co-authorship networks. We used tools like ScientoPy and VOSviewer to assess key metrics such as the number of publications, citations, and influential contributors. Findings/Results: The study reveals significant trends, with a peak in research productivity in 2022, reflecting heightened global attention on early physical health. China, Japan, and Iran led contributions, with China dominating in publication volume. Notable researchers included Saeed Ghorbani and emerging contributors Ahmad Chaeroni and Amir Dana. Leading journals, such as the International Journal of Environmental Research and Public Health and Frontiers in Public Health, were prominent sources. Conclusion: Despite a decline in productivity post-2022, PA and PF research for preschoolers remains important. These shifting trends provide opportunities for researchers to explore new areas or innovate approaches. Significant contributions from Asian researchers also indicate a geographic shift in the global research focus. This study also highlights the importance of physical activity in preschoolers and how trends in scientific publications can help policymakers formulate more effective interventions to improve children's fitness.

Identifying and Visualizing Global Research Trends and Hotspots of Artificial Intelligence in Medical Ultrasound: A Bibliometric Analysis.

Applications of artificial intelligence (AI) in medical ultrasound have rapidly grown in recent years. Therefore, it is necessary to identify and visualize global research trends and hotspots of AI in medical ultrasound to provide guidance for further exploitation. This study aims to highlight the global research trends and hotspots of the top 100 most-cited papers related to AI in medical ultrasound by combining quantitative and visualization methods. Articles on AI in medical ultrasound were selected from the WoSCC database and ranked by citation count. After identifying the 100 most-cited papers, we conducted a quantitative and visualized analysis of bibliometric characteristics, including leading research countries, prominent institutions, key authors and journals, author clusters and collaborations, and keyword co-occurrence network analysis. The top 100 highly cited papers from the WoSCC database were published between 1999 and 2021, with total citations ranging from 91 to 1580. The most cited article was published in IEEE Transactions on Medical Imaging. The top three most prolific countries/regions were the United States, mainland China, and the United Kingdom. The most published institutions and journals were Idaho University and IEEE Transactions on Medical Imaging. Twelve authors published more than four papers, with Suri, JS being the most productive author. The most studied topics were "ultrasound", "computer-aided diagnosis", and "segmentation". Ultrasonography of Superficial Organs was the main site that was studied the most. This study provides comprehensive insights into the characteristics of AI in medical ultrasound through quantitative and visualized analysis of the most highly cited literature. It serves as a valuable reference for the development and applications of AI, fostering potential collaborations within this domain.