Artificial intelligence in sepsis management: A bibliometric analysis

Artificial intelligence (AI) has revolutionized sports science, advancing performance analysis, injury prevention, and strategy optimization. However, its long-term impact remains underexplored. This study conducts a bibliometric and predictive analysis of -driven sports research over the past decade, identifying key contributors, emerging trends, and future directions through visualization techniques. Method: A systematic review was conducted on related sports research from 2014 to 2024 using the Web of Science Core Collection. Bibliometric tools Citespace and Vosviewer were employed to analyze publication trends, author networks, institutional collaborations, and keyword co-occurrences. Polynomial regression analysis was applied to forecast future research growth based on historical publication and citation trends. A total of 5,811 publications with 96,753 citations were identified. China, the United States, and the United Kingdom were the most productive countries, with China leading in volume but exhibiting lower citation impact. The Chinese Academy of Sciences, Stanford University, and the University of Oxford were the top research institutions. Keyword analysis revealed that "machine learning," "deep learning," and "computer vision" were the most studied topics, while emerging themes such as "stress analysis," "information processing," and "pose estimation" indicated shifts towards driven real-time monitoring and predictive analytics. Polynomial regression models predicted continued research growth, with publication trends following y = 354x ² + 1350x + 528 (r ² = 0.94) and citation growth modeled as y = 9680x ² + 24900x + 7850 (r ² = 0.99), suggesting sustained acceleration in AI applications within sports science. The integration of sports science has grown rapidly, with machine learning and computer vision playing a pivotal role in optimizing athletic performance, real-time feedback, and injury prevention. Predictive analytics and driven modeling can transform sports training by personalizing programs based on biomechanical and physiological data, enhancing both performance and injury resilience. However, disparities in AI adoption across regions and institutions highlight the need for greater international collaboration, particularly in developing regions where access to driven sports technologies is limited. Future research should refine -driven models for individualized training, integrate wearable sensor data for precision, and address ethical concerns. Additionally, policymakers and sports organizations should invest in AI-based training and health monitoring systems to bridge the gap between technology-rich and technology-limited regions. As it evolves, its role in sports science will expand, driving advancements in performance analysis, health monitoring, and strategic decision-making.

Background and objectivesMetabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is one of the leading causes of hepatocellular carcinoma (HCC). Over the years, significant progress has been made in diagnosing and treating MASLD. However, there is a lack of bibliometric analysis exploring the relationship between MASLD and HCC. This study aims to comprehensively analyze the knowledge structure and research status of MASLD-related HCC using bibliometric methods.MethodsThe study was conducted using the Web of Science Core Collection (WoSCC) database, retrieving literature related to MASLD and HCC from 2020 to 2024. VOSviewer, CiteSpace, and the R package “bibliometrics” were used for data analysis.ResultsThe study included 2,576 articles from 97 countries. The results showed that the number of publications related to MASLD and HCC increased steadily until 2023, after which there was a slight decline. China (The People’s Republic of China) is the country with the highest volume of research output, while the United States is the country with the highest number of citations. The University of California System, the Chinese University of Hong Kong, and the Egyptian Knowledge Bank were the leading contributing institutions. The International Journal of Molecular Sciences, Cancers, and Liver International were the most influential journals in this field, with the International Journal of Molecular Sciences being the most co-cited journal, and the Journal of Hepatology had the highest local impact in the field, as evaluated by the H-index. The publications were authored by 13,851 researchers, with Vincent Wai-Sun Wong, Masato Yoneda, and Rohit Loomba being the most influential authors in the field of MASLD and HCC. Rohit Loomba and Hashem B. El-Serag were the most cited authors. Trend analysis indicated that “tissue”, “element-binding protein”, and “contributes” were the most frequently studied topics in 2024, suggesting emerging research trends.ConclusionThis study provides a comprehensive summary of the research trends and developments in HCC related to MASLD over the past five years. The findings reveal the latest research frontiers and hotspots in this field, offering valuable insights for future research. However, it should be noted that although the WoSCC is a widely recognized and authoritative database, it may have limitations related to regional bias, language coverage, and metadata accuracy, which could potentially affect the comprehensiveness of our analysis.

Artificial intelligence (AI) has increasingly influenced healthcare, yet its adoption in nursing research remains underexplored. Although interest in AI applications, such as robotics, decision support, and machine learning, is growing, a comprehensive bibliometric mapping of global scholarship in nursing remains lacking. To examine the global trends, intellectual structures, and thematic evolution of AI-related research in nursing from 1984 to 2025 (through May 2025) and to identify key authors, institutions, and research foci. A systematic search was conducted in the Web of Science Core Collection using predefined title-based keywords ("nurse" OR "nursing") AND ("artificial intelligence" OR "machine learning" OR "deep learning" OR "robotic∗" OR "chatbot∗" OR "neural network∗"). Studies were included if they explicitly addressed nursing practice, management, education, or research applications of AI. Two independent reviewers screened all records by title and abstract to confirm nursing relevance using predefined inclusion and exclusion criteria. This yielded 799 records; after removing duplicates and uncited papers, 530 publications were retained as nursing-related. Bibliometric analysis employed performance and science-mapping techniques via VOSviewer and Biblioshiny. Indicators included publication trends, citation distributions, keyword co-occurrence, authorship patterns, and collaborations. The number of publications increased substantially after 2018, with the United States, China, and the United Kingdom being the most productive countries. Dominant research themes included robotics in elder care, clinical decision support systems, and nursing education enhanced by AI tools. Coauthorship analysis revealed limited international collaboration, and keyword mapping identified "robotics," "machine learning," and "nursing education" as leading focal points. Despite decades of development, AI remains an emerging and underutilized area within nursing research. The rapid growth of publications in recent years signals expanding interest, yet the field lacks consolidated efforts and cohesive global collaboration. Greater interdisciplinary and international engagement is needed to accelerate innovation.

The application of artificial intelligence (AI) in medicine has garnered significant attention in recent years, offering new possibilities for improving patient care across various domains. For herpes zoster, a viral infection caused by the reactivation of the varicella-zoster virus, AI technologies have shown remarkable potential in enhancing disease diagnosis, treatment, and management. This study aims to investigate the current research status in the use of AI for herpes zoster, offering a comprehensive synthesis of existing advancements. A systematic literature review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Three databases of Web of Science Core Collection, PubMed, and IEEE were searched to identify relevant studies on AI applications in herpes zoster research on November 17, 2023. Inclusion criteria were as follows: (1) research articles, (2) published in English, (3) involving actual AI applications, and (4) focusing on herpes zoster. Exclusion criteria comprised nonresearch articles, non-English papers, and studies only mentioning AI without application. Two independent clinicians screened the studies, with a third senior clinician resolving disagreements. In total, 26 articles were included. Data were extracted on AI task types; algorithms; data sources; data types; and clinical applications in diagnosis, treatment, and management. Trend analysis revealed an increasing annual interest in AI applications for herpes zoster. Hospital-derived data were the primary source (15/26, 57.7%), followed by public databases (6/26, 23.1%) and internet data (5/26, 19.2%). Medical images (9/26, 34.6%) and electronic medical records (7/26, 26.9%) were the most commonly used data types. Classification tasks (85.2%) dominated AI applications, with neural networks, particularly multilayer perceptron and convolutional neural networks being the most frequently used algorithms. AI applications were analyzed across three domains: (1) diagnosis, where mobile deep neural networks, convolutional neural network ensemble models, and mixed-scale attention-based models have improved diagnostic accuracy and efficiency; (2) treatment, where machine learning models, such as deep autoencoders combined with functional magnetic resonance imaging, electroencephalography, and clinical data, have enhanced treatment outcome predictions; and (3) management, where AI has facilitated case identification, epidemiological research, health care burden assessment, and risk factor exploration for postherpetic neuralgia and other complications. Overall, this study provides a comprehensive overview of AI applications in herpes zoster from clinical, data, and algorithmic perspectives, offering valuable insights for future research in this rapidly evolving field. AI has significantly advanced herpes zoster research by enhancing diagnostic accuracy, predicting treatment outcomes, and optimizing disease management. However, several limitations exist, including potential omissions from excluding databases like Embase and Scopus, language bias due to the inclusion of only English publications, and the risk of subjective bias in study selection. Broader studies and continuous updates are needed to fully capture the scope of AI applications in herpes zoster in the future.

Age-related macular degeneration (AMD) represents a significant clinical concern, particularly in aging populations, and recent advancements in artificial intelligence (AI) have catalyzed substantial research interest in this domain. Despite the growing body of literature, there remains a need for a comprehensive, quantitative analysis to delineate key trends and emerging areas in the field of AI applications in AMD. This bibliometric analysis sought to systematically evaluate the landscape of AI-focused research on AMD to illuminate publication patterns, influential contributors, and focal research trends. Using the Web of Science Core Collection (WoSCC), a search was conducted to retrieve relevant publications from 1992 to 2023. This analysis involved an array of bibliometric indicators to map the evolution of AI research in AMD, assessing parameters such as publication volume, national/regional and institutional contributions, journal impact, author influence, and emerging research hotspots. Visualization tools, including Bibliometrix, CiteSpace and VOSviewer, were employed to generate comprehensive assessments of the data. A total of 1,721 publications were identified, with the USA leading in publication output and the University of Melbourne as the most prolific institution. The journal Investigative Ophthalmology & Visual Science published the highest number of articles, and Schmidt-Eerfurth emerged as the most active author. Keyword and clustering analyses, along with citation burst detection, revealed three distinct research stages within the field from 1992 to 2023. Presently, research efforts are concentrated on developing deep learning (DL) models for AMD diagnosis and progression prediction. Prominent emerging themes include early detection, risk stratification, and treatment efficacy prediction. The integration of large language models (LLMs) and vision-language models (VLMs) for enhanced image processing also represents a novel research frontier. This bibliometric analysis provides a structured overview of prevailing research trends and emerging directions in AI applications for AMD. These findings furnish valuable insights to guide future research and foster collaborative advancements in this evolving field.

The integration of artificial intelligence (AI) into medicine has ushered an era of unprecedented innovation, with substantial impacts on healthcare delivery and patient outcomes. Understanding the current development, primary research focuses, and key contributors in AI applications in medicine through bibliometric analysis is essential. For this research, we utilized the Web of Science Core Collection as our main database and performed a review of literature covering the period from January 2019 to December 2023. VOSviewer and R-bibliometrix were performed to conduct bibliometric analysis and network visualization, including the number of publications, countries, journals, citations, authors, and keywords. A total of 1,811 publications on research for AI in medicine were released across 565 journals by 12,376 authors affiliated with 3,583 institutions from 97 countries. The United States became the foremost producer of scholarly works, significantly impacting the field. Harvard Medical School exhibited the highest publication count among all institutions. The Journal of Medical Internet Research achieved the highest H-index (19), publication count (76), and total citations (1,495). Four keyword clusters were identified, covering AI applications in digital health, COVID-19 and ChatGPT, precision medicine, and public health epidemiology. "Outcomes" and "Risk" demonstrated a notable upward trend, indicating the utilization of AI in engaging with clinicians and patients to discuss patients' health condition risks, foreshadowing future research focal points. Analyzing our bibliometric data allowed us to identify progress, focus areas, and emerging fields in AI for medicine, pointing to potential future research directions. Since 2019, there has been a steady rise in publications related to AI in medicine, indicating its rapid growth. In addition, we reviewed journals and significant publications to pinpoint prominent countries, institutions, and academics. Researchers will gain important insights into the current landscape, collaborative frameworks, and key research topics in the field from this study. The findings suggest directions for future research.

IntroductionSince the significant breakthroughs in artificial intelligence (AI) algorithms, the application of AI in bladder cancer has rapidly expanded. AI can be used in all aspects of the bladder cancer field, including diagnosis, treatment and prognosis prediction. Nowadays, these technologies have an excellent medical auxiliary effect and are in explosive development, which has aroused the intense interest of researchers. This study will provide an in-depth analysis using bibliometric analysis to explore the trends in this field.MethodDocuments regarding the application of AI in bladder cancer from 2000 to 2022 were searched and extracted from the Web of Science Core Collection. These publications were analyzed by bibliometric analysis software (CiteSpace, Vosviewer) to visualize the relationship between countries/regions, institutions, journals, authors, references, keywords.ResultsWe analyzed a total of 2368 publications. Since 2016, the number of publications in the field of AI in bladder cancer has increased rapidly and reached a breathtaking annual growth rate of 43.98% in 2019. The U.S. has the largest research scale, the highest study level and the most significant financial support. The University of North Carolina is the institution with the highest level of research. EUROPEAN UROLOGY is the most influential journal with an impact factor of 24.267 and a total citation of 11,848. Wiklund P. has the highest number of publications, and Menon M. has the highest number of total citations. We also find hot research topics within the area through references and keywords analysis, which include two main parts: AI models for the diagnosis and prediction of bladder cancer and novel robotic-assisted surgery for bladder cancer radicalization and urinary diversion.ConclusionAI application in bladder cancer is widely studied worldwide and has shown an explosive growth trend since the 21st century. AI-based diagnostic and predictive models will be the next protagonists in this field. Meanwhile, the robot-assisted surgery is still a hot topic and it is worth exploring the application of AI in it. The advancement and application of algorithms will be a massive driving force in this field.

IntroductionThis study aims to systematically map the intellectual landscape and emerging trends in lipid metabolism research within hepatocellular carcinoma from 2014 to 2024.MethodsA total of 607 peer-reviewed publications were retrieved from the Web of Science Core Collection and PubMed. Bibliometric and visualization tools, including VOSviewer and CiteSpace, were employed to perform data analysis, including keyword co-occurrence and cluster analysis.ResultsWe identified a significant surge in research activity, with 53.05% of the total literature published in the last three years. China and the USA emerged as the leading contributors, with the University of California System and the journal Cancers being the most prolific institution and publication outlet, respectively. Current research hotspots are centered on the mechanisms by which oxidative stress drives the transformation of non-alcoholic fatty liver disease into hepatocellular carcinoma. Furthermore, three critical frontiers for future investigation were identified: (1) the regulatory role of PPARγ in lipid metabolic reprogramming and its therapeutic implications; (2) the molecular mechanisms of the farnesoid X receptor in modulating bile acid metabolism during hepatocarcinogenesis; and (3) the NF-κB signaling pathways that mediate metabolic shifts and confer chemoresistance in liver cancer.DiscussionThese findings provide a comprehensive reference for prioritizing research directions and therapeutic target discovery in the metabolic-related oncology domain.

BackgroundInvestigating gut microbiota during pregnancy is vital for understanding maternal–infant health, pregnancy-related disease mechanisms, and offspring development. While research in this field has grown rapidly, systematic analyses of global trends, collaborative networks, and thematic evolution remain limited. This bibliometric study maps the developmental landscape of “gut microbiota during pregnancy,” identifying research priorities and future directions.MethodsA bibliometric analysis of pregnancy and gut microbiota studies (1991–2025) was conducted using the Web of Science Core Collection and Scopus databases. Publications were analyzed using bibliometrix, VOSviewer, and CiteSpace to evaluate publication trends, research contributions, collaboration networks, keyword co-occurrence patterns, and thematic evolution.ResultsThe analysis encompassed 5,432 (Web of Science Core Collection) and 5,542 (Scopus) publications, with an annual growth rate exceeding 15%. Research output has grown exponentially since 2014. The China and United States were the most productive countries, with the United States demonstrating the highest total citations and a central role in global collaborative networks. Key influential institutions included the University of Turku, University College Cork, and the Chinese Academy of Sciences. Leading authors were Collado, Maria Carmen; Tain, You-Lin; and Cryan, John F. The research was highly interdisciplinary, spanning microbiology, nutrition, immunology, and medicine. Core journals disseminating knowledge were Nutrients, Frontiers in Microbiology, and Gut Microbes. High-impact and co-cited references established the knowledge foundation, focusing on maternal microbiome remodeling, delivery mode’s impact, and the gut-brain axis. Keyword analysis revealed a thematic evolution from initial descriptive studies of microbial composition to recent investigations into mechanisms linking microbiota to gestational diabetes mellitus, preeclampsia, preterm birth, and neurodevelopmental outcomes via the gut-brain axis.ConclusionThis study presents an integrative bibliometric analysis of global research on gut microbiota during pregnancy, delineating its rapid evolution and current intellectual structure. The field has matured from descriptive ecology to mechanistic and translational research, with strong international collaboration and interdisciplinary integration. The identified research fronts, including the interplay between microbial dysbiosis and specific pregnancy complications, as well as the influence of the maternal gut microbiome on offspring neurodevelopment, represent promising avenues for future investigation.

In recent decades, artificial intelligence (AI) has played an increasingly important role in medicine, including dermatology. Worldwide, numerous studies have reported on AI applications in dermatology, rapidly increasing interest in this field. However, no bibliometric studies have been conducted to evaluate the past, present, or future of this topic. This study aimed to illustrate past and present research and outline future directions for global research on AI applications in dermatology using bibliometric analysis. We conducted an online search of the Web of Science Core Collection database to identify scientific papers on AI applications in dermatology. The bibliometric metadata of each selected paper were extracted, analyzed, and visualized using VOS viewer and Cite Space. A total of 406 papers, comprising 8 randomized controlled trials and 20 prospective studies, were deemed eligible for inclusion. The United States had the highest number of papers (n = 166). The University of California System (n = 24) and Allan C. Halpern (n = 11) were the institution and author with the highest number of papers, respectively. Based on keyword co-occurrence analysis, the studies were categorized into 9 distinct clusters, with clusters 2, 3, and 7 containing keywords with the latest average publication year. Wound progression prediction using machine learning, the integration of AI into teledermatology, and applications of the algorithms in skin diseases, are the current research priorities and will remain future research aims in this field.

Current Global Research Trends of Tethered Cord Syndrome Surgery: A Scientometric and Visual Analysis

Background and PurposeChronic atrophic gastritis (CAG), affecting approximately 20–30% in high-risk populations, contributes to significant morbidity and mortality due to its progression to gastric cancer. Despite two decades of research into its pathogenesis, the vast body of literature has not yet been systematically mapped. A comprehensive bibliometric analysis mapping the field’s evolution, collaborative networks, and knowledge gaps remains lacking. Therefore, we conduct a 20-year bibliometric analysis (2005–2024) of research on the mechanism of CAG to identify seminal works, emerging themes, evaluate global collaboration networks, and highlight translational challenges and opportunities.Patients and MethodsData were retrieved from the Web of Science Core Collection (WoSCC) spanning from January 1, 2005, to December 31, 2024. Bibliometric analysis was performed using CiteSpace and VOSviewer to analyze publication trends, influential authors and institutions, keyword clusters, and citation bursts.ResultsA total of 954 papers were identified, with China leading in publication output (41.51%), followed by the USA (15.20%). The USA demonstrated high centrality in international collaboration. Key journals included WORLD J GASTROENTERO and GASTROENTEROLOGY. Prolific authors such as Liu Yuetao and co-cited authors like CORREA P were identified. Keyword analysis revealed “Helicobacter pylori” as the most prominent term, with clusters focusing on traditional Chinese medicine, macrophage biology, and gastric intestinal metaplasia.ConclusionThe study highlights the significant research output and collaboration in CAG, emphasizing the importance of interdisciplinary approaches and international partnerships. Future research should focus on integrating traditional knowledge with modern mechanistic studies and addressing emerging themes such as microbiome dysbiosis and precision medicine.

This study aimed to explore the current status, hotspots, and trends in research on polyunsaturated fatty acids (PUFAs) and cardiovascular inflammation. Literature related to cardiovascular inflammation and PUFAs from the Web of Science Core Collection between January 1, 2009, and July 1, 2024, was retrieved using specific search terms, and the data were visualized and analyzed using CiteSpace (version 6.3.R3) and VOSviewer (version 1.6.20). PUFAs and cardiovascular inflammation publications have been found to grow over the past 15 years. The United States leads in this field, with Nutrients being the leading journal. Harvard University and the University of California system are the most influential institutions. Most publications are in immunology and medical journals, while citations are primarily in biology and nutrition journals. International collaboration is strong, with Mozaffarian and Calder making significant contributions. Bhatt et al. and Bosch et al. have the most citations and the highest centrality, respectively. Research trends focus on the relationship between PUFAs and inflammatory markers, the anti-inflammatory mechanisms of PUFAs, and their association with diseases. Omega-3 and omega-6 PUFAs show divergent impacts on inflammation and cardiovascular diseases (CVD) risk, with gut microbiota mediating bidirectional regulation. Emerging research directions in PUFAs' anti-inflammatory mechanisms encompass gut microbiota, oxidative stress, and pro-resolving mechanisms, with gut microbiota as the current focus. This bibliometric analysis has highlighted sustained growth and global collaboration in PUFAs-cardiovascular inflammation research. Key emerging mechanisms have been found to focus on the gut-microbiota-PUFAs axis, oxidative stress, and pro-resolving pathways, pointing towards future translational opportunities.

As the urgency of addressing climate change grows, strategies such as developing zero-emission campuses to achieve carbon neutrality are becoming increasingly crucial. Yet, research in this field remains somewhat underdeveloped and fragmented. This study aims to bridge this gap, providing a scientometric analysis of the research conducted on zero-emission campuses from 1997 to 2023, using data from the Web of Science Core Collection. The study analyzed 1009 bibliographic records with the aid of CiteSpace software, focusing on identifying key co-authors, co-words, co-citations, and clusters. The findings indicate a rapid increase in research in the field of zero-emission campuses, with a significant surge in the number of publications in recent years, culminating in 174 in 2021 alone. The leading universities in terms of publication count were the University of California System, Egyptian Knowledge Bank, and the Chinese Academy of Sciences. Furthermore, the United States, China, and the United Kingdom were identified as the main contributing countries/regions to publishing in this field, indicating a broad, global collaboration. The scope of research has broadened from technical elements, such as energy, to encompass social factors that influence sustainability. Emerging research areas were identified, including education and sustainability, renewable energy and energy efficiency, campus planning and design, waste management and recycling, policy support, and pro-environmental behavior. This study provides a structured overview of the research landscape in the field of zero-emission campuses, offering valuable guidance for academics and encouraging further collaboration. The identified research clusters, notable authors, and influential institutions hold significant implications for policy decisions, industry practices, and the implementation of zero-emission strategies on campuses, aiding in the broader pursuit of sustainability.

In recent decades, dramatic changes in modern environmental exposures and lifestyles have resulted in a steep rise in the prevalence of allergic diseases such as asthma, allergic rhinitis, atopic dermatitis and food allergies. Evidence is mounting that the microbiota plays a crucial role in allergic disorder development and evolution. Therefore, a better understanding of allergic diseases within the context of the microbiota is urgently needed. This work aimed to comprehensively outline general characteristics, research hotspots, evolution routes, and emerging trends in this area. Relevant publications from January 2002 to December 2021 were obtained from the Web of Science Core Collection on 5 August 2022. Bibliometric and visual analyses were performed using CiteSpace; VOSviewer; an online bibliometric platform; and Microsoft Excel 2019. In total, 2535 documents met the requirements. The annual number of publications has shown rapid growth in the last two decades. The USA, University of California System, and Isolauri E of the University of Turku were the most productive and influential country, institution, and author, respectively. The Journal of Allergy and Clinical Immunology was the most prolific and most cocited journal. High-frequency keywords included "gut microbiota", "asthma", "atopic dermatitis", "children", and "probiotics". Recent studies have focused on "atopic dermatitis", "skin", "asthma", and "probiotics", according to the cocitation analysis of references. Burst detection analysis of keywords showed that "community", "skin microbiome", "microbiome", "Staphylococcus aureus", and "chain fatty acid" were emerging research frontiers, which currently have ongoing bursts. In the last 20 years, studies of the microbiota in allergic diseases have been flourishing, and the themes have been increasing in depth. These findings provide valuable references on the current research hotspots and gaps and development trends in the link between the microbiota and allergic diseases.