A174: Visualization Analysis of Artificial Intelligence Applications in Sports

Artificial intelligence in sepsis management: A bibliometric analysis

Deep learning has become a key technology in enhancing sports performance, offering new possibilities for training optimization. This study aims to (1) explore the research progress on deep learning applications in enhancing sports performance and (2) analyze future trends in this field. Method: Data from 545 English-language publications (2015-2025) were extracted from the Web of Science Core Collection using keywords related to DL (e.g., CNN, LSTM) and sports performance (e.g., athletic training, motion analysis). CiteSpace 6.4R was employed for bibliometric, co-word, and burst-term analyses. 1) Since 2015, research in this field has steadily increased before plateauing, with the most significant rise from 2021 (73 articles) to 2022 (90 articles) and a peak in 2024 (107 articles). Leading contributors include China (206 publications), the U.S., Germany, Japan, Korea, India, and the U.K. 2) ImageNet Classification with Deep Convolutional Neural Networks is the most cited work, introducing AlexNet and demonstrating its performance on large-scale image classification, significantly influencing computer vision. This study explores deep learning’s application in sports performance, with most literature focusing on specific sports. 3) High-frequency keywords in sports include motion analysis, performance, task analysis, kinematics, gait analysis, and sports training, while in computing, they include deep learning, convolutional neural networks, activity recognition, human pose estimation, classification, and wearable sensors. 4) Research trends are reflected in emergent keywords. From 2015–2019, studies emphasized motion detection, image recognition, kinematics, and image motion analysis for improved motion monitoring and assessment. From 2020–2025, a shift toward velocity, task analysis, gait analysis, and three-dimensional displays highlights a focus on individualized assessment and optimized training. Pose estimation, sports training, IoT, and virtual reality remain active, underscoring the role of deep learning in intelligent sports performance analysis. 1) Over the past decade, research on deep learning for sports performance has grown rapidly before stabilizing, with China, the U.S., Germany, Japan, Korea, India, and the U.K. leading the field. 2) The focus has shifted from general motion detection to refined applications like gait analysis, velocity estimation, and task analysis, though most studies prioritize computational methods over sport-specific needs. 3) Research trends emphasize integrating deep learning, including CNNs and pose estimation, into performance analysis. While progress has been made in motion monitoring, further exploration of sport-specific applications is needed. 4) Advancements in AI and sports science will drive intelligent training, injury prevention, and performance optimization, benefiting both competitive sports and public health.

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.

Artificial intelligence (AI) has revolutionized optical coherence tomography (OCT) imaging for the better detection of retinal diseases like diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma. Here, bibliometric analysis of AI in OCT is presented. We conducted a systematic search in the Web of Science Core Collection (WoSCC) database up to January 22, 2025 with the help of AI and OCT-related terms. The journal articles and reviews on the topic were examined based on publication trends, author productivity, institution affiliation, and keyword co-occurrence. Visualization was performed through VOSviewer software (1.6.20). A total of 765 articles were analyzed with an increasing trend in publications, led by the USA (30.7%), China (23.3%), and the UK (11.1%) in research production. The most cited institution was the Medical University of Vienna (9.0%), and the most productive journal was Scientific Reports (7.3%). The most common keywords were "Optical Coherence Tomography", "Deep Learning", and "Diabetic Retinopathy" with frequency of 469, 297, 221, respectively. Our review of AI-enhanced OCT for retinal diseases was both comprehensive and systematic, identifying key trends and current research hotspots. As research productivity in this field continues to grow, the focus is increasingly shifting toward developing more accurate AI-driven imaging techniques to improve the diagnosis of various retinal diseases. Future work should prioritize algorithm validation and clinical implementation to facilitate widespread adoption

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.

Over the recent years, firms have showed an increased demand for leveraging the performance potential of Artificial Intelligence (AI) technologies. However, the general applicability of AI to many settings raises important managerial questions about whether aspects such as potential for in-house versus external AI development, the presence of complementary technological resources (e.g. access to cloud computing), or even the specific use of AI (e.g. product/service development, operational management) would guide firm-level decisions underlying AI adoption. While recent research has provided some insight on worker-level decisions about using AI, knowledge about firm-level AI adoption, as well as heterogeneities in adoption, remains scant. To address this empirical gap, this preliminary paper provides one of the first comprehensive assessments of firm-level determinants of AI adoption while accounting for the heterogeneous sources, applications, and complementary technological resources of AI in organizations. Specifically, this paper reports the first results of a novel firm-level AI adoption survey deployed to a representative sample of South Korean firms from 2017 and 2018. Econometric assessments identified that firm size and use of existing intangible assets are important for AI adoption. However, while these characteristics are significant for AI adoption regardless of whether the technology was produced in-house or obtained from a vendor, analyses suggest that there may be heterogeneities on the determinants of AI adoption depending on the operational application of AI. Furthermore, AI adoption also ensues in tandem with the adoption of other digital technologies (e.g. big data, cloud computing, and the Internet of Things) and with contemporaneous firm reorganization.

Although the rapid development of Artificial Intelligence (AI) in recent years has brought increasing academic attention to the intelligent transformation of physical education, the core knowledge structure of this field, such as its primary research topics, has yet to be systematically explored. The LDA (latent Dirichlet allocation) topic model can identify latent themes in large-scale textual data, helping researchers extract key research directions and development trends from extensive literature. This study is based on data from the Web of Science Core Collection and employs a systematic literature screening process, utilizing the LDA topic model for in-depth analysis of relevant literature to reveal the current status and trends of AI technology in physical education. The findings indicate that AI applications in this field primarily focus on three areas: “AI and data-driven optimization of physical education and training”, “computer vision and AI-based movement behavior recognition and training optimization”, and “AI and virtual technology-driven innovation and assessment in physical education”. An in-depth analysis of existing research shows that the intelligentization of physical education, particularly in school and athletic training contexts, not only promotes sustainable development in the field but also significantly enhances teaching quality and safety, allowing educators to utilize data more precisely to optimize teaching strategies. However, current research remains relatively broad and lacks more precise and robust data support. Therefore, this study critically examines the limitations of current research in the field and proposes key research directions for further advancing the intelligent transformation of physical education, providing a solid theoretical framework and guidance for future research.

The rapid rise of artificial intelligence (AI) is revolutionizing the way that companies view data, technology and process automation across all industries from marketing to financial decisions. Through this quantitative study the essay will examine how AI predictive analytics may help to enhance marketing performance and financial strategy outcomes for SMEs. To develop data, 350 management and analytics professionals working for organizations that had implemented AI-driven predictive tools were surveyed in a cross-sectional survey design. This paper aimed to measure the influence of artificial intelligence (AI) and machine learning (ML) applications in marketing KPIs and financial performance KPIs. As a whole, our findings suggest that the adoption of AI is quite positively related to advancements in marketing and financial performance, leaving those not adopting AI behind. Regression results suggest that the depth of AI implementation is a key factor in the enhancement of performance. We then included the important moderators as data quality, zero-momentum and non-generation. These are the takeaways: Businesses can get huge strategic advantages from moving to AI, only if they have good data governance, digital infrastructure and people trained in use of the technology. The variance analysis also reflects that some industries are more receptive to the integration of artificial intelligence, such as finance and service industries, due to a possibility for stronger data capacity and better data-processing approaches. The results from this investigation reveal that today’s AI-powered predictive analytics can function as a strategic asset to businesses by boosting operational effectiveness, strengthening financial wellbeing and directing better marketing choices. But companies must also spend on the basics, like quality of data, skills of staff and governance around ethical use of AI, to make the most from predictive capabilities. This research contributes to the growing literature on AI strategy and arm business leaders with actionable information that can help them be more competitive in a data-driven global economy.

Burning fossil fuels is a major contributor to global warming and climate change, threatening biodiversity and presenting challenges for scientists and policymakers. To address these issues and promote sustainable development, bioenergy is based on efficiently utilizing biomass resources such as biomass feedstock for energy. Moreover, intensified global changes adversely affect our environment, signifying the importance of biomass and energy conservation research as a re-emerging topic of scientific interest. This study aims to comprehensively review the existing research trends, knowledge gaps, and future research in this field. A scientometric analysis was conducted using CiteSpace based on 1177 articles retrieved from the Web of Science Core Collection from 2000 to 2021. The leading countries, authors, and institutions are the USA, the Chinese Academy of Science, and Prof. Dr. Mehdi Bidabadi, respectively. The “Biomass and Bioenergy” is a prominent journal. European and North American countries collectively contribute around 73% of the publications. While there has been an increasing trend in publications over time, effective cooperation among institutions and authors, especially in developing countries, remains weak. The current research hotspots include “metabolic pathway”, “<i>Clostridium ljungdahlii</i>”, “short-term harvesting”, “anaerobic digestion”, “environmental impacts”, and so on. Future research will focus on cutting-edge techniques, the potential of biomass feedstocks for bioenergy, and the role of biomass feedstocks in mitigating climate change. This review provides comprehensive and valuable information for researchers, institutions, and policymakers to develop an effective, environmentally friendly, and sustainable strategy to cope with global environmental issues.

Drought, one of the most destructive natural disasters, profoundly impacts ecosystems, agricultural production, water resource security, and socio-economic development. In recent years, machine learning has emerged as a central approach to drought prediction, owing to its capacity to integrate multi-source data and improve accuracy. Using the Web of Science Core Collection and VOSviewer, this study conducts bibliometric analyses to trace research development, clarify knowledge structures, and identify emerging directions of machine learning in drought prediction from 1999 to 2025. Results show a steady upward trend in publications and citations, with rapid growth since 2019, reflecting sustained global scholarly attention. Analysis of the productivity of countries, institutions, journals, and authors reveals that China and the USA lead in research output, supported by several highly productive institutions and widely recognized journals. Author keyword co-occurrence analysis identifies three thematic clusters: (1) traditional meteorological and hydrological drought studies focused on indices such as the SPI and the SPEI; (2) integrated frameworks combining machine learning with remote sensing; and (3) advanced deep learning approaches, including LSTM and Transformer models, incorporating variables such as soil moisture, precipitation, and runoff, along with climate drivers such as the ENSO and extreme events. Overall, these results demonstrate a transition from single-index models to integrated prediction systems combining diverse datasets with advanced algorithms, alongside increasing focus on climate–drought interactions and extremes. These findings guide enhancing model interpretability, advancing real-time early warning, and supporting drought management under climate change.

This study explores the integration of Artificial Intelligence (AI) into the Reverse Logistics Process (RLP) to enhance decision-making, operational efficiency, and resource recovery. Through a systematic literature review, the applications of AI across critical RLP stages (planning, execution and control) are identified. Key findings highlight the transformative role of AI in optimizing network design, improving product collection and inspection, and supporting decision-making for the disposition of recovered items. AI technologies and functionalities such as machine learning, predictive analytics, and decision-support systems demonstrate significant potential for automating complex processes, reducing operational costs, and improving logistical precision. However, barriers to AI adoption in the RLP include high implementation costs, organisational resistance, lack of specialist personnel, and limited technological infrastructure. The review also identifies enablers such as advancements in the Internet of Things, blockchain, and big data analytics, which facilitate AI adoption and integration in RLP. The study concludes that AI is essential for establishing resilient and adaptive RL systems and offers substantial opportunities to address uncertainty while improving efficiency and decision-making. Future research should focus on overcoming adoption barriers, advancing predictive models, and integrating AI with other Industry 4.0 technologies to enhance RLP outcomes. This review fills a significant gap in the literature by offering a structured analytical framework that categorises AI applications based on decision types and RLP stages, providing an integrated perspective not previously addressed in the field.

With the rapid development of virtual reality, augmented reality, mixed reality, and artificial intelligence, exhibition space design and communication have undergone significant transformations. This study systematically reviews the research progress using the Web of Science Core Collection as the data source. Publications from 2015 to 2025 were analyzed through bibliometric and visualization tools, including R-biblioshiny, CiteSpace, and VOSviewer. The analysis examined annual publication trends, keyword co-occurrence and bursts, thematic evolution, and national and institutional collaboration networks. Results show a steady growth in studies, with peaks in 2018 and 2023, reflecting strong academic interest in immersive exhibition technologies. “Virtual reality” and “augmented reality” remain central themes, while “cultural heritage”, “exhibition design”, and “user experience” are major application domains. Emerging topics such as “artificial intelligence”, “virtual environments”, and “data visualization” highlight a shift toward intelligence-driven and experience-centered research. China and the United States lead in output and influence internationally, whereas European countries show regional collaboration. Despite these advances, limitations persist regarding intelligent applications, cross-cultural comparisons, longitudinal research, theoretical development, and sustainability concerns. By mapping knowledge structures and research frontiers, this study offers valuable insights for advancing academic inquiry and guiding innovative practices in exhibition spaces.

Artificial Intelligence (AI) is transforming the sports industry by enhancing athletic performance, improving predictive analytics, managing health, boosting fan engagement, and ensuring accurate officiating. This review provides a comprehensive examination of AI's applications in sports, drawing from a systematic literature search across databases such as PubMed, IEEE Xplore, and Google Scholar. The methodology involved selecting recent, relevant studies that contribute to understanding AI's impact in sports, with data extraction focusing on AI applications, specific sports contexts, observed benefits, challenges, and future opportunities. In enhancing athletic performance, AI analyzes data from sensors, cameras, and wearable devices to identify strengths and weaknesses, optimizing training programs. Examples include AI-driven platforms in football and basketball that track player positions and actions, providing actionable insights for coaches. Predictive analytics, another crucial AI application, involves forecasting game outcomes and evaluating player performance through historical and current data, aiding in strategic decision-making. AI also plays a vital role in health management and injury prevention by analyzing biomechanical and physiological data to predict potential injuries and monitor recovery, ensuring athletes' well-being and prolonging careers. Fan experience is significantly enhanced through personalized recommendations, virtual assistants, and augmented reality experiences, deepening engagement and satisfaction. Officiating benefits from AI technologies like VAR in football, which review and analyze controversial decisions to ensure accuracy and fairness. Despite the promising applications, challenges such as data quality, cost, and ethical concerns, including privacy and bias, remain. Future opportunities lie in developing real-time feedback systems and integrating AI with the Internet of Things (IoT) for comprehensive data collection, further revolutionizing performance analysis and injury prevention. This review highlights AI's transformative potential in sports, emphasizing the need for high-quality data, cost-effective solutions, and ethical considerations to maximize its benefits and address existing challenges.

Sonodynamic therapy and combination strategies for cancer: a bibliometric analysis and knowledge mapping

In recent decades, more and more studies have been conducted on source appointment of heavy metals, since they can accumulate in the food chain and have a negative impact on the ecological environment and human health. However, almost never before had scholars tried to make a comprehensive and methodical review in this field from the scientometric and bibliometric perspective. The purpose of this review is to offer insights into the research topics and trend evaluation in terms of source appointment of heavy metals over time using the visualization and analysis software, CiteSpace. We retrieved a total of 2,533 articles from the Web of Science Core Collection (WoSCC) dated between 1994 and 2022, and analysed the progress, hotspots, and trends in this field by synthesized networks of cooperation analysis, co-citation analysis, keyword co-occurrence and cluster analysis, and keywords burst analysis. The overall development of the topic can be divided into four periods, and the rapid development began from 2010. Environmental Sciences was the leading subject category, and the journal Science of the Total Environment had the highest number of publications (9.51%), which was most cited as well (2,390 times). China published the most articles in this field, in which Chinese Academy of Sciences was the leading institution. Said Muhammad and Xinwei Lu were the top two most productive authors. According to citation frequency, Hakanson L was the movers and shakers. Keyword co-occurrence and cluster analysis results showed that “the health risk assessment,” “lake sediments”, “trace elements,” “positive matrix factorization,” “air pollution,” “road dust,” and “megacity” are likely to be hotspots. The “particulate matter,” “China,” “sediments” and “road dust” demonstrated the research tendencies of this domain by keyword burst analysis.