Innovations in Visual Arts Driven by Artificial Intelligence: Global Research Hotspots and Emerging Trends

Innovations in Visual Arts Driven by Artificial Intelligence: Global Research Hotspots and Emerging Trends | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Innovations in Visual Arts Driven by Artificial Intelligence: Global Research Hotspots and Emerging Trends ZHUOFAN YANG, SHUAI YANG, JIAXINHUA WANG, QITAO WU This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7766646/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The rapid integration of artificial intelligence (AI) and visual arts has driven profound paradigm shifts in art creation, market dynamics, and cultural exchange. This bibliometric analysis of 379 publications from the Web of Science Core Collection (2001–2024) describes the current applications of AI in visual arts and future challenges. We cre-ated a visual collaborative network mapping connecting AI and visual arts using biblio-metric methods, which included knowledge foundations, research trends, nation-al/institutional contributions, and emerging frontiers. While clustering analysis identifies current research objectives focused on Generative Adversarial Networks (GANs), co-citation analysis demonstrates theoretical progress within the field. Important conclusions show that China leads the world in academic output and influence, while the US has the highest level of international collaboration. Humanities, Arts, and Computer Science are among the core areas, with Applied Sciences-Basel (ISSN 2076–3417) emerging as the most prolific journal. While developing paths prioritize eco-conscious generative models, neurocognitive mechanisms of human-AI co-creation, and environment-responsive AI art design, current research focuses on algorithmic advancements in AI-generated art, ethical judgments, and changes in art markets. This study establishes a framework for comprehending the evolution in AI-driven visual arts and provides empirical insights to inform technological practices and multidisciplinary synergies. Humanities/Complex networks Social science/Complex networks Physical sciences/Mathematics and computing Social science/Science technology and society Artificial intelligence Visual arts Bibliometrics Citespace VOSviewer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Recently, the application of artificial intelligence (AI) technology in the arts driving revolutionary changes in artistic creation, art markets, and cultural exchange[ 1 – 3 ]. AI not only enhances traditional art forms through generative approaches but also offers unique research perspectives for cultural integration, art project creation, and environmental art design[ 4 – 6 ]. This technology has played a crucial role in promoting artistic creation, facilitating cultural exchange, and expanding the boundaries of the art market[ 7 – 9 ]. Furthermore, the application of AI in artistic creation has altered the creative processes, enabling the interaction and inspiration between AI and human artists[ 5 , 6 , 10 ]. However, the controversies remain in the originality of artistic works, the cultural identity, and the balance between technological innovation and human creativity[ 11 – 13 ]. Meanwhile, technologies such as generative adversarial networks (GANs) have emerged as a creative tool for artistic creation, facilitating style transfer and generating new art models[ 14 – 16 ]. The rapid advancement of generative models, alongside the deep application of AI, makes the cognitive feedback and emotional experience of the public at the risk of being marginalized[ 17 – 19 ]. This imbalance between technology and humanistic perspective may bring profound challenges to the mechanism of human-AI collaborative creation[ 20 – 22 ]. Despite the expansion of artificial intelligence (AI) research in the arts, the development of its knowledge framework has not kept up with technological advancements[ 23 – 25 ]. The rapid accumulation of literature in this field exposed significant challenges in academic integration: the significance of basic literature remains undefined, collaborative networks among key academic communities require clarification, and systematic tracking of knowledge evolution and paradigm shifts is absent[ 26 – 28 ]. In this context, bibliometrics emerges as a vital tool for quantitatively analyzing disciplinary development, especially in studies involving AI and visual arts[ 29 – 31 ]. Through techniques such as citation network analysis and co-occurrence clustering, bibliometrics facilitates the precise identification of domain knowledge structures, dynamic tracking of academic communities, and localization of key knowledge nodes, thereby offering data-driven insights into disciplinary trajectories[ 32 – 34 ]. For interdisciplinary fields, bibliometric analysis helps to overcome the subjective limitations of traditional literature reviews by revealing hidden research pathways through objective data and explain the dissemination mechanisms of core literature[ 35 – 37 ]. Based on this, our study employs bibliometric methods for system analysis of 379 peer-reviewed articles indexed in the Web of Science Core Collection from 2001 to 2024, focusing on AI applications in the visual arts. The research aims to elucidate the core issues, evolutionary trends, and future directions within this field. The following research questions (RQs) will be discussed in this investigation: RQ1: What are the research trends and major journal distributions in AI-driven visual arts studies over the past 24 years? RQ2: Which nations, institutions, and researchers emerge as the predominant contributors in this disciplinary domain? RQ3: What constitutes the foundational knowledge base and seminal literature in AI-visual arts research? RQ4: What are the current research hotspots and developmental trajectories in AI-enabled visual arts? 2. Materials and Methods 2.1 Data Sources and Search Strategy The data for this study were sourced from the Web of Science (WOS) Core Collection, a globally recognized and authoritative platform for academic literature retrieval and analysis, developed and maintained by Clarivate Analytics[ 38 – 40 ]. The WOS Core Collection indexes high-quality journals across various disciplines, including natural sciences, social sciences, humanities, and the arts, ensuring the scientific validity and representativeness of the analysis through its comprehensive coverage and high reliability[ 41 , 42 ]. A thematic search strategy was devised to intersect "Artificial Intelligence" and "Visual Arts" using the following query: TS=("Artificial Intelligence" OR "AI") AND TS=("Visual Arts" OR "Digital Art" OR "Generative Art" OR "Media Art" OR "Interactive Art" OR "Computer-Generated Art" OR "AI-Generated Art" OR "Creative Design" OR "3D Art" OR "Graphic Design" OR "Aesthetic Experience" OR "Installation Art" OR "New Media Art" OR "Algorithmic Art" OR "Painting" OR "Sculpture").The search covered publications from 2001 to 2024, yielding an initial corpus of 698 articles. Document types were restricted to "Article" and "Review," and the language was limited to English. After applying these filters, 319 irrelevant entries were excluded based on predefined criteria: News Item (n = 1), Meeting Abstracts (n = 2), Retraction Notices (n = 3), Book Chapters (n = 5), Editorial Material (n = 10), Retracted Publications (n = 16), Early Access articles (n = 36), Proceeding Papers (n = 222), and Non-English publications (n = 24). The final dataset comprised 379 publications, including 362 research articles and 17 review papers. The data retrieval and processing workflow is illustrated in Fig. 1 . 2.2 Research Method Bibliometrics is a mathematical and statistical approach that quantitatively analyzes scientific literature, focusing on its production, dissemination, and impact[ 43 – 45 ]. As a key branch of scientometrics, this method reveals the knowledge structure, research hotspots, developmental trends, and patterns of scientific communication within specific fields[ 46 – 48 ]. By examining metrics such as publication volume, authorship, institutional contributions, keywords, journal distribution, and citation networks, bibliometrics offers a systematic overview of scientific research, with applications in academic research, scientific policy formulation, and disciplinary evaluation[ 49 , 50 ]. This study utilized Microsoft Excel 2021 to analyze publication trends from 2001 to 2024. VOSviewer 1.6.18 and Scimago Graphica were employed to investigate co-occurrence and temporal variations among countries, institutions, authors, and keywords. In the generated visualizations, nodes are represented as spheres with text labels, where sphere size corresponds to node significance. Distinct colors indicate different clusters, while connecting lines illustrate co-occurrence relationships, with line thickness reflecting the strength of these relationships. CiteSpace 6.1.R1 (developed by Chaomei Chen, China) was used to visualize journal dual-overlay and document co-citation analyses. For co-citation mapping, CiteSpace parameters were configured as follows: time slices spanning 2001 to 2024, a slice length of one year, and selection criteria (k = 25). In the co-citation network, spheres represent cited references, with size proportional to citation frequency. Connections between spheres denote co-citation relationships. The concentric rings within each sphere illustrate the number of citations and their corresponding time periods, with ring size and color indicating citation volume and temporal distribution, respectively. 3. Results 3.1 The distributions, relationships and contributions of journals in the visual arts field The analysis of annual publication output trends provides critical insights into the development of research field and the hotspots of academic attention and serves as a foundation for understanding the emergence, expansion, and potential research directions in the future [ 51 – 53 ]. We illustrate the annual publication output in this field from 2001 to 2024, result shows that the field has progressed through distinct phases, transforming from slow growth to rapid expansion ( Fig. 2 ). Between 2001 and 2014, the annual publication output remained relatively low, with only 1 to 3 publications created per year. A marked increase occurred after 2015, with exponential growth observed between 2018 and 2024, rising from 17 publications in 2018 to 103 in 2024. This surge underscores accelerated advancements in the field, indicating its transition into an active research phase. This growth pattern aligns closely with the widespread adoption of artificial intelligence technologies and their deepening integration into interdisciplinary applications. Curve-fitting analysis reveals a strong correlation between publication trends and a quartic polynomial model (equation: \(\:\varvec{y}=0.0191{\varvec{x}}^{4}-0.5706{\varvec{x}}^{3}+5.6318{\varvec{x}}^{2}-18.747\varvec{x}+20.167\) ), achieving an exceptional goodness-of-fit (R² = 0.9981), which demonstrates robust development in this field. Current analysis suggests an annual volume will continue to rise in the coming years, which is closely related to the popularity of artificial intelligence technology and its in-depth application in interdisciplinary fields. According to the Web of Science classifications, the 379 publications are distributed in 109 different categories. We list the top 20 discipline categories by publication volume (Table 1 ). Among 41 publications, the most prolific category is Humanities Multidisciplinary, followed closely by Art with 40 publications, Computer Science Information Systems with 38 publications, Computer Science Artificial Intelligence with 33 publications, and Engineering Electrical Electronic with 31 publications. These high-productivity disciplines reflect the extensive attention and in-depth exploration of this interdisciplinary research theme within the global academic community. Moreover, the adoption of interdisciplinary methodologies has provided diverse perspectives and a robust theoretical foundation for the application of artificial intelligence in visual arts fields[ 54 , 55 ]. This multidisciplinary integration not only fosters innovation within the field but also opens new directions for future research[ 56 – 58 ]. Table 1 Top 20 disciplines by number of publications Rank Web of Science Categories NP Rank Web of Science Categories NP 1 Humanities Multidisciplinary 41 11 Chemistry Multidisciplinary 15 2 Art 40 12 Computer Science Interdisciplinary Applications 15 3 Computer Science Information Systems 38 13 Education Educational Research 14 4 Computer Science Artificial Intelligence 33 14 Chemistry Analytical 13 5 Engineering Electrical Electronic 31 15 Physics Applied 13 6 Materials Science Multidisciplinary 25 16 Neurosciences 12 7 Telecommunications 24 17 Computer Science Theory Methods 11 8 Computer Science Software Engineering 21 18 Philosophy 11 9 Multidisciplinary Sciences 17 19 Psychology Multidisciplinary 11 10 Engineering Multidisciplinary 16 20 Spectroscopy 11 NP: Number of Publications Next, we sought to utilized the dual-map overlay, which is a visualization method that integrates maps generated by CiteSpace, providing an intuitive illustration of interdisciplinary journal relationships [ 59 – 61 ]. This approach effectively highlights the connections between citing fields (on the left) and cited fields (on the right), thereby revealing knowledge flow across disciplines at the journal level. For the clustering analysis, the built-in Z-score algorithm in the software was employed to process the data. The results demonstrate distinct clusters that reflect the directional flow of disciplinary knowledge, the citation relationships between citing journals (left) and cited journals (right) ( Fig. 3 ) . The former represents applied domains, while the latter reflect disciplinary foundations. Colored connecting lines denote citation pathways between journals in distinct fields, highlighting publication and citation activities across these domains. Key citation trajectories originate from 'SYSTEMS, COMPUTING, COMPUTER' and 'PSYCHOLOGY, EDUCATION, SOCIAL,' extending toward emerging research frontiers such as 'MATHEMATICS, SYSTEMS, MATHEMATICAL' and 'PSYCHOLOGY, EDUCATION, HEALTH,' respectively. Notably, the citation pathway from 'SYSTEMS, COMPUTING, COMPUTER' to 'MATHEMATICS, SYSTEMS, MATHEMATICAL' exhibits the highest Z-score (z = 5.12), underscoring its critical role in knowledge dissemination and interdisciplinary influence. We also generated the top ten journals ranked by publication citation frequency ( Table 2 ) . And Applied Sciences - Basel from Switzerland (Impact Factor IF = 2.5) has the highest number of published papers, with a total of 11 papers, followed by Artnodes (IF = 0.2) from Spain and the Journal of Cultural Heritage (IF = 3.5) from France, which published 10 and 9 articles, respectively. These findings indicate the prominent role of these journals within the research field and their significant contributions to advancing knowledge in this domain. Notably, the Journal of Cultural Heritage (IF = 3.5) from France boasts the highest impact factor among the ten journals listed. The right panel of Table 2 highlights the top ten journals and conferences ranked by citation count. ArXiv ranks first with 363 citations, followed by the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and Lecture Notes in Computer Science (LNCS), with 224 and 166 citations, respectively. This illustrates the substantial academic influence of these journals and conferences, reflecting their pivotal role in knowledge dissemination and the advancement of the field. Table 2 Top 10 journals or conferences by number of publications and citations Rank Journals NP Country IF(JCR2023) Cited journals or meetings NC Country IF (JCR2023) 1 APPLIED SCIENCES-BASEL 11 Switzerland 2.5 ARXIV 363 \ \ 2 ARTNODES 10 Spain 0.2 PROC CVPR IEEE 224 \ \ 3 JOURNAL OF CULTURAL HERITAGE 9 France 3.5 LECT NOTES COMPUT SC 166 \ \ 4 IEEE ACCESS 8 USA 3.4 IEEE ACCESS 92 USA 3.4 5 SUSTAINABILITY 7 Switzerland 3.3 COMPUT HUM BEHAV 75 USA 9 6 COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 7 USA 0 ADV NEUR IN 72 \ \ 7 HELIYON 6 United Kingdom 3.4 PLOS ONE 72 USA 2.9 8 AI & SOCIETY 6 Germany 2.9 LEONARDO 72 USA 0.4 9 ARTS 6 Switzerland 0.3 IEEE I CONF COMP VIS 71 \ \ 10 JOURNAL OF SCIENCE AND TECHNOLOGY OF THE ARTS 5 Portugal 0.2 FRONT PSYCHOL 64 Switzerland 2.6 In conclusion, the above analysis illustrates that the publications in visual arts field exhibit rapid expansion, while closely related to “MATHEMATICS, SYSTEMS, MATHEMATICAL” and published by different kinds of journals. 3.2 National, individual and institutional scholarly contributions Analyzing collaborations among countries and regions reveals valuable insights into research cooperation patterns and intensity, which reflect scientific capabilities, resource-sharing practices, and international influence within a given field[ 62 – 64 ]. This study encompasses contributions from 55 countries in terms of scholarly outputs and list the top ten countries ranked by publication volume in Table 3 . Among them, China has the largest number of publications with a total of 137 papers, followed by the United States with 64 of them. The United Kingdom exhibits the highest average citations per publication, followed by Austria and the United States, indicating a relatively higher research quality in these nations. In contrast, Russia and South Korea display lower average citations per publication, suggesting potential improvement of their academic outputs. Furthermore, China and the United States achieve the highest H-index scores, indicating their dominant scholarly influence in this research domain. Table 3 Top 10 countries by number of publications Rank Country NP NC AC H-index 1 China 137 713 5.20 12 2 USA 64 669 10.45 12 3 United Kingdom 31 438 14.13 9 4 Germany 19 125 6.58 6 5 Spain 18 99 5.50 4 6 South Korea 14 34 2.43 3 7 Italy 12 90 7.50 3 8 Australia 12 57 4.75 5 9 Austria 9 106 11.78 5 10 Russia 9 2 0.22 1 A visual analysis of the collaborative relationships among countries and regions was conducted using VOSviewer software ( Fig. 4 ) . The analysis was configured with a minimum threshold of five publications per country, based on this we generating a collaborative network map that reveals the distribution and intensity of international partnerships. In the visualization, the size of the nodes corresponds to national publication output, with larger circles indicating higher productivity. The thickness of the lines reflects the strength of bilateral collaboration, where thicker connections denote closer cooperation, while the gradient color of the lines signifies the overall collaborative intensity between a country and its partners. The connecting lines highlight China as the most prominent country for international collaboration, demonstrating robust partnerships with multiple nations, particularly the United States, Australia, and the United Kingdom. The United States emerge as the secondary nodal center, with significant collaborative ties to both China and the UK, underscoring its pivotal role in global academic networks. In contrast, several countries, including several European nations and Russia, exhibit comparatively weaker collaborative linkages, suggesting opportunities for enhanced integration into international scholarly networks. The analysis of author contributions reveals collaborative relationships among scholars and the structures of their academic networks, thereby identifying core researchers who play leading roles in specific research domains. In this field, a total of 1,144 authors participated in the relevant studies. Table 4 presents the top ten authors ranked by publication count. Among these authors, Lin Rungtai and Lyu Yanru had the highest number of publications, each publishing four articles. Other prolific contributors included Goel Ashok K. and Grba Dejan, each publishing three articles. Cetinic Eva achieved the highest average citation count per paper, indicating the superior academic quality of their work and its recognition within the field. Additionally, Lin Rungtai and Lyu Yanru exhibited the highest H-index, further underscoring their scholarly influence in this domain. Table 4 Top 10 authors by number of publications Rank Author NP NC AC H-index 1 Lin, Rungtai 4 54 13.50 3 2 Lyu, Yanru 4 54 13.50 3 3 Goel, Ashok K. 3 99 33.00 2 4 Grba, Dejan 3 6 2.00 2 5 Cetinic, Eva 2 249 124.50 2 6 Altenburger, P 2 66 33.00 2 7 Busse, Hj 2 66 33.00 2 8 Kämpfer, P 2 66 33.00 2 9 Lubitz, W 2 66 33.00 2 10 Schumann, P 2 66 33.00 2 This study utilized VOSviewer software to perform a visualization analysis of the collaborative relationships among authors. By establishing a minimum publication threshold of two articles per author, a collaborative network map comprising 37 authors was generated (Fig. 5 ). In this visualization, nodes represent individual authors, while the connecting lines signify collaborative partnerships. As shown in Fig. 5 , the author collaboration network presents both closely-connected core research teams and peripheral loosely-cooperating groups. The most prominent cluster (in red) centers around Lubitz, W., and Schumann, P., demonstrating strong academic cohesion and disciplinary dominance through intensive intra-team collaboration. A secondary collaborative network emerges around scholars such as Kupczyk, Erwin, and Mueller, Constanze, which exhibits notable collaborative research tendencies, though with lower interaction density compared to the red cluster. Meanwhile, independent researchers like Cetinic, Eva, and Spjuth, Ola appear with limited collaborative ties to other scholars, reflecting their relatively limited academic influence. These patterns indicate substantial potential for expanding and optimizing collaborative networks within this research domain. Institutional analysis reveals the research landscape and key contributors within a field, providing scholars with insights into the academic influence and collaborative networks of prominent institutions[ 65 – 67 ]. This analysis also serves as a reference for future research planning and cross-institutional collaboration. In the artificial intelligence (AI) applications in visual arts research, a total of 593 institutions have participated in relevant studies. The top ten institutions ranked by publication volume are listed in Table 5 , illustrating the distribution of research and the academic impact of these organizations. The Hong Kong University of Science and Technology (HKUST) leads with six published papers, achieving a total citation count of 132 and an average of 22.00 citations per paper, significantly surpassing other institutions. This underscores the substantial academic influence and international recognition of HKUST’s research in this field. Zhejiang University follows closely with five publications, accumulating a total of 57 citations and an average of 11.40 citations per paper. Institutions such as Macau University of Science and Technology, the Chinese Academy of Sciences, and Nanyang Technological University also contributed five publications each, demonstrating the active engagement of institutions from Mainland China and Singapore in this domain. Notably, the Georgia Institute of Technology, despite its relatively lower publication count, achieved a total citation count of 110 and the highest average citations per paper of 27.50 among all institutions. This highlights the exceptional depth and broad academic impact of its research contributions. Table 5 Top 10 institutions by number of publications Rank Organization NP NC AC H-index 1 HONG KONG UNIV SCI & TECHNOL 6 132 22.00 3 2 ZHEJIANG UNIV 5 57 11.40 3 3 MACAU UNIV SCI & TECHNOL 5 39 7.80 2 4 CHINESE ACAD SCI 5 36 7.20 3 5 NANYANG TECHNOL UNIV 5 34 6.80 3 6 GEORGIA INST TECHNOL 4 110 27.50 3 7 BEIJING TECHNOL & BUSINESS UNIV 4 54 13.50 3 8 NATL TAIWAN UNIV ARTS 4 54 13.50 3 9 SHANGHAI JIAO TONG UNIV 4 41 10.25 2 10 WUHAN UNIV TECHNOL 4 13 3.25 1 This study utilized VOSviewer to visualize the collaborative relationships among institutions. By establishing a minimum publication threshold of two papers per institution, a collaborative network consisting of 78 institutions was generated (Fig. 6 ). In the network diagram, nodes represent institutions, while connecting lines indicate collaborative partnerships. The analysis reveals that Shanghai Jiao Tong University, Zhejiang University, and the Hong Kong University of Science and Technology (HKUST) constitute a central collaborative hub, highlighting China’s strong research capabilities in this field. Moreover, institutions such as Nanyang Technological University, Stanford University, and the University of Cambridge act as critical nodes within international collaboration networks, playing a significant role in advancing the field. Looking ahead, enhancing global collaborative networks—especially by fostering connections between regional research clusters—could effectively promote synergistic innovation and sustained development in this area. 3.3 The evolution of hotspots in visual arts 1) Co-citation analysis Co-citation analysis allows scholars to uncover latent relationships, emerging themes, and the evolution of disciplines within a knowledge domain[ 68 – 70 ]. A co-citation relationship is established when two publications (A and B) are cited together by a third publication (C). In this study, we utilized a g-index (k = 25) to extract 524 references from the 379 included articles. Subsequent co-citation and clustering analyses were performed to delineate the core intellectual framework and trace the evolution of research hotspots in this field. After analysis, we present the co-citation network of references, highlighting the 10 most frequently cited works (Fig. 7 ). Each node is labeled with the first author’s name and publication year. The largest node in the network corresponds to the article "Art, Creativity, and the Potential of Artificial Intelligence" by Mazzone and Marian et al. (2019), published in ARTS, which holds the highest citation count (17 citations). This underscores its significant influence within the research domain. The study focuses on the artificial intelligence art creation system "AICAN," examining the application of AI in 21st-century artistic practices and its implications for redefining art and artists. The authors argue that AICAN’s outputs should be recognized as legitimate artworks and advocate for collaborative frameworks between human and machine creativity to fully harness their combined potential. Building on this analysis, the study further conducts a cluster network analysis of the references. Using the log-likelihood ratio (LLR) algorithm, cluster labels were extracted from title fields, identifying five major clusters among the 524 references. The clustering results indicate a Q-value of 0.9321 (exceeding the threshold of 0.3), confirming a robust cluster structure, and an S-value of 0.9215 (above 0.7), demonstrating high reliability. The cluster network comprises the following five key themes: #0 "creating art," #1 "AI-based painting system," #2 "artificial intelligence," #7 "critical inquiry," and #17 "co-creative drawing system" ( Fig. 8 ) . The phenomenon of citation bursts reflects rapidly emerging research foci within specific time periods[ 71 – 73 ]. A higher burst value indicates that a reference has exerted greater influence during its active timeframe. This metric serves as a valuable tool for identifying research frontiers and tracing the evolutionary trajectory of prominent thematic trends within a discipline[ 74 – 76 ]. Figure 9 illustrates the citation bursts identified in our analysis. Among the references with high burst strength that remain influential today, a representative example is "Putting the Art in Artificial: Aesthetic Responses to Computer-Generated Art." This study investigates public evaluations of visual artworks created by humans versus those generated by artificial intelligence (AI). The findings reveal a persistent bias against computer-generated art, with participants attributing lower aesthetic value to such works. However, this bias diminishes when observers witness the robotic creation process, as the act of observation fosters anthropomorphic attributions to the computational system. Another notable work exhibiting a strong burst is "Artificial Intelligence, Artists, and Art: Attitudes Toward Artwork Produced by Humans vs. Artificial Intelligence." This research explores public perceptions of AI-generated artworks and examines how the artist’s identity (human or AI) influences evaluations of artistic merit. The study demonstrates that while distinctions exist in the perceived artistic value of human versus AI-created works, the attribution of authorship to AI does not result in a statistically significant reduction in overall artistic appraisal. 2) Co-occurrence Evolution Co-occurrence analysis examines the relationships between frequently occurring keywords and terms in scholarly literature[ 77 , 78 ]. This method seeks to delineate the thematic structure of a research field, identify connections among studies, synthesize historical research trends, and predict possible future research directions [ 79 , 80 ]. This study conducted a keyword clustering analysis using VOSviewer software. By establishing a minimum keywords occurrence threshold of three, 119 keywords were selected to construct a keyword co-occurrence visualization map (Fig. 10 ). The map comprises nodes represented by circles and labels, where the size of each node corresponds to the frequency of the keywords, and the thickness of the connecting lines reflects the strength of the relationships between keywords. The colors of the nodes denote distinct research domains, forming five primary clusters. Cluster #1: Technical Foundations of Deep Learning and Computer Vision This cluster focuses on key technical terms such as "deep learning," "machine learning," and "convolutional neural networks (CNNs)." It emphasizes the crucial role of deep learning in AI-driven visual art applications, specifically in image generation, classification, and style transfer. Techniques such as "style transfer" and "generative adversarial networks (GANs)" are particularly significant in art generation and digital image processing. Additionally, keywords associated with "paintings" suggest a research emphasis on the digitization of traditional artistic styles and the creative reconstruction enabled by deep learning. Cluster #2: Computational Creativity and Generative Art This cluster emphasizes keywords such as "generative art," "creativity," and "computational creativity," reflecting explorations into AI-driven innovation in artistic creation. Terms like "intention" and "algorithmic frameworks" underscore efforts to simulate or enhance human creative intent through computational models. Additionally, associations with "education" and "robotics" suggest the expanding applications of generative art into cross-disciplinary educational contexts and interactive creation. Cluster #3: Virtual Reality and Technological Expansion This cluster is anchored by the themes of "virtual reality" (VR) and "technology," reflecting the profound integration of artificial intelligence with cutting-edge technological advancements. Keywords such as "augmented reality," "interactive art," and "metaverse" illustrate that VR and metaverse technologies are creating novel mediums and possibilities for artistic creation. Furthermore, the interconnectedness of "natural language processing" (NLP) and "image processing" highlights a broader trend in artificial intelligence toward the development of multimodal interaction systems and the design of immersive art experiences. Cluster #4: Holistic Applications of AI and Ethical Considerations This cluster revolves around artificial intelligence (AI) and generative artificial intelligence (GAI), encompassing keywords such as ethics, innovation, and media art. It highlights the widespread adoption of AI in visual arts, along with its societal impacts and the ethical controversies it raises. The emphasis on ethics underscores academic concerns regarding the misuse of technology, cultural conflicts, and the accountability associated with AI-generated art. Cluster #5: Artistic Design and User Experience This cluster revolves around keywords such as "design," "model," and "interactivity," highlighting the application of artificial intelligence (AI) in optimizing artistic design and enhancing user experience. The prominence of terms like "information" and "perception" indicates that research efforts are concentrated on improving the interactivity and efficiency of information delivery in AI-driven artistic works. Furthermore, the inclusion of concepts such as "sustainability" and the "technology acceptance model (TAM)" emphasizes the importance of ensuring the sustainable development of AI technologies within the realm of artistic design. The further analysis expands upon the keyword clustering analysis presented in Fig. 10 by illustrating the temporally weighted distribution of keywords ( Fig. 11 ) . The color gradient, ranging from dark blue to bright yellow, signifies the chronological prominence of research themes, with dark blue representing early-stage focal areas and bright yellow indicating recent trends. The analysis reveals that early research primarily concentrated on foundational technological investigations, such as 'deep learning,' 'machine learning,' and 'classification.' These keywords highlight the initial focus on applying artificial intelligence (AI) to fundamental visual art tasks, including image recognition, feature extraction, and model optimization. With the development of technology, research has gradually shifted towards higher-level applications. Keywords such as 'generative art,' 'AI art,' and 'computational creativity' emerged as central themes, marking significant advancements in AI-driven artistic creation. Notably, the adoption of generative adversarial networks (GANs) facilitated the production of AI-generated artworks, signifying a paradigm shift in creative methodologies. Since 2022, research trajectories have diversified to emphasize human-centric and experiential dimensions. Keywords like 'media art,' 'augmented reality (AR),' 'virtual reality (VR),' and 'interactivity' underscore a shift from static image generation to multisensory immersive experiences. This evolution illustrates AI's expanding role in dynamic, multidimensional artistic expression. Concurrently, terms such as 'generative AI,' 'technology,' and 'social media' highlight the increasing prominence of generative AI systems (e.g., GPT, DALL-E) as leading research topics. Recent studies not only investigate technical innovations but also explore their societal implications, including applications in public engagement, cultural discourse, and collaborative creativity. 4. Discussion An analysis of artificial intelligence (AI) applications in visual arts reveals a complex developmental trajectory characterized by dynamically shifting research priorities and focal topics across different periods. Emerging research directions often represent expansions or refinements of existing themes, thus, the changes in research hotspots and directions are dynamic. Although previous studies have reviewed and analyzed AI applications in visual arts, they have yet to establish a cohesive knowledge framework that allows readers to intuitively obtain key insights. Therefore, there is an urgent need to construct a novel, comprehensive, and intuitive theoretical framework for the application of artificial intelligence in visual art, providing both overview and guidance for future research ( Fig. 12 ) . These disciplinary categories reflect the global academic community's extensive attention and in-depth exploration of this interdisciplinary research theme. Through disciplinary analysis, we can identify entry points for artificial intelligence applications in the visual arts and their complex theoretical foundations, thereby revealing the research potential and scholarly value of the field. The collaborative network analysis reveals dynamic partnerships among geographic regions, authors, and institutions. This study identifies the leading collaborative entities within the field, providing researchers with reference points for identifying potential collaborators and suitable research domains. At the regional level, China, the United States, and the United Kingdom exhibit the highest levels of collaborative activity. Among individual researchers, Lin Rungtai, Lyu Yanru, and Ashok K. Goel stand out as the most prolific collaborators. The institutional analysis highlights three primary contributors: Hong Kong University of Science and Technology (HKUST), Zhejiang University (ZJU), and Macau University of Science and Technology (MUST). The bibliographic coupling analysis identifies key co-cited publications, including works by Mazzone and Elgammal (2019), Hong and Curran (2019), and Rombach et al. (2022). These studies not only advance the integration of artificial intelligence and art through technical innovations but also establish critical theoretical and practical frameworks that underpin the field's evolution. The development of research hotspots highlights important problems and new developments in the area, defining central themes and possible turning points at various times [ 81 , 82 ]. The purpose of this analysis is to forecast subjects that are likely to become prominent, identify future research avenues, and clarify the trajectory of domain evolution. Initially, research efforts focused mostly on fundamental ideas like "deep learning," "machine learning," and "classification," which reflected preliminary investigations into the use of artificial intelligence (AI) technology in the visual arts. In the intermediate stage, "generative art," "AI art," and "computational creativity" emerged as key themes as research focus shifted to higher-level applications. This change represented significant advances in AI-powered creative production. Diversification has become more and more important in recent trends after 2022, marked by keywords such as "media art," "augmented reality," "virtual reality (VR)," and "interactivity." These developments indicate that AI's role in visual arts has expanded beyond static art generation to encompass dynamic, experiential domains. Notably, the emergence of terms like "generative AI," "technology," and "social media" underscores the growing prominence of generative AI systems (e.g., GPT and DALL-E) as cutting-edge research topics in visual arts. These insights provide valuable guidance for future scholars. By synthesizing high-frequency keywords alongside those exhibiting elevated betweenness centrality, a mapping of research hotspots was generated. Following the exclusion of foundational keywords such as 'artificial intelligence' and 'visual art,' 14 high-frequency and high-centrality keywords were identified and categorized into “Generative Art and Computational Creativity “ [ 83 , 84 ], “Virtual Reality (VR) and Augmented Reality (AR)”[ 85 , 86 ], “Interactive Art and Multimodal Experiences” [ 87 ] and “Generative AI and Social Media” [ 88 ] four thematic clusters. 5. Future Trends and challenges Artificial intelligence exhibits substantial technological advantages in invigorating artistic production[ 89 ]. Generative Adversarial Networks (GANs) and deep learning models have reached significant milestones in image generation, as evidenced by the works of Mario Klingemann and Google's DeepDream project. Future advancements in these technologies are poised to enhance the complexity and expressiveness of creative outputs, facilitating breakthroughs in image quality and realism. This progress will enable high-fidelity restoration and innovative integration of diverse material textures and light/shadow effects, continually pushing the boundaries of artistic practice. The integration of AI and art lies in its capacity to catalyze cross-disciplinary collaboration[ 90 ]. For instance, interactive art creation necessitates expertise that spans computer science, artistic design, and psychology. Moving forward, this interdisciplinary synergy is expected to intensify, driving breakthroughs in the conceptual frameworks, formal expressions, and social value dimensions of artistic practice. Projects such as TeamLab's immersive installations and the Tangible Media Group's research exemplify how cross-domain innovation can propel the evolution of artistic disciplines. As artificial intelligence (AI) becomes increasingly prevalent in the realm of art creation, addressing ethical challenges-especially those related to copyright attribution becomes imperative [ 91 , 92 ]. The rise of AI-generated artworks necessitates the establishment of legal frameworks that are comparable to those governing traditional art, ensuring clear copyright ownership and protecting the rights of stakeholders. Well-defined regulations will mitigate ambiguities that could obstruct artistic production and dissemination, paralleling the necessity for established authorship in conventional art to safeguard the interests of creators [ 93 ]. AI's robust data-processing capabilities empower personalized artistic creation and cultural hybridization[ 94 ]. By analyzing diverse cultural and artistic elements, AI tools can generate bespoke creative materials for artists. For instance, generative AI systems produce culturally distinctive artworks tailored to specific heritage contexts. This capability fosters cross-cultural exchange and mutual enrichment within the arts, thereby enhancing global artistic diversity—similar to how artists from varied backgrounds leverage technology to synthesize unique aesthetic expressions. The application of artificial intelligence in the field of visual arts has brought new tools for education and cultural dissemination [ 95 ]. Digital art platforms that utilize AI enable the rapid and large-scale distribution of artworks and cultural information, transcending geographic and linguistic barriers. For instance, VR-enhanced online art courses provide global learners with immersive experiences, thereby strengthening educational engagement and fostering intercultural dialogue[ 96 , 97 ]. Such innovations amplify the societal impact of art and facilitate shared cultural prosperity, paralleling the ways in which traditional museums leverage digital platforms to broaden their reach[ 98 ]. While advancements in artificial intelligence (AI) present significant opportunities in the visual arts, they also pose multiple challenges. These challenges include issues of artistic originality, cultural interpretation, the balance between technology and creativity, and ethical concerns including “ambiguity in artistic originality and authenticity”,” cultural misinterpretation and expression biases”,” imbalance between technical execution and creative innovation “and “copyright and legal frameworks issue” In traditional art creation, the artist's unique thoughts, emotions, cultural background and creative skills are the core of the work's originality [ 99 ]. In contrast, AI - driven creation is difficult to determine whether the work stems from the mechanical generation [ 100 ].Besides, existing copyright laws, which were originally designed for human-authored works, exhibit significant limitation when applied to AI-generated art[ 101 ]. AI systems employed in the visual arts rely on extensive datasets that may encompass sensitive personal information or unauthorized artworks[ 102 , 103 ]. The use of non-compliant data sources or improper utilization could result in violations of privacy and intellectual property rights. 6. Conclusions This study employs bibliometric methods to systematically analyze research trends, knowledge structures, and developmental trajectories in the application of artificial intelligence (AI) to visual arts from 2001 to 2024. By integrating 379 publications from the Web of Science Core Collection and utilizing tools such as CiteSpace and VOSviewer, we elucidate the field's research hotspots, international collaboration networks, core knowledge bases, and emerging frontiers. Over the past 24 years, research on artificial intelligence in visual arts has evolved from a phase of gradual exploration (2001–2014, with an annual average of 1–3 publications) to exponential growth (2018–2024, with annual outputs ranging from 17 to 103 publications). This trajectory closely aligns with technological breakthroughs in generative models—such as GANs and diffusion models—and their cross-disciplinary integration. A quartic polynomial fitting model (R² = 0.9981) predicts sustained research activity in the coming years. A multi-tiered analysis encompassing national, institutional, and individual scholarly contributions reveals distinct geographic and collaborative dynamics in AI-driven visual arts research. China, with 137 publications, and the United States, with 64 publications, dominate the global output in terms of sheer volume. Meanwhile, the United Kingdom stands out as a benchmark for academic influence, boasting an average of 14.13 citations per paper. At the institutional level, the Hong Kong University of Science and Technology, with 6 publications and a total of 132 citations, along with Zhejiang University, which has 5 publications and 57 total citations, emphasizes interdisciplinary technical-artistic exploration. Conversely, the Georgia Institute of Technology demonstrates exceptional impact per paper, with an average of 27.50 citations. The international collaboration network underscores China’s pivotal role as a hub, facilitating cross-border knowledge exchange through robust partnerships with the U.S. and Australia. At the individual level, Lin Rungtai, with 4 publications, drives applied innovation through intensive collaboration, while Cetinic Eva establishes theoretical foundations for the field through high-impact publications, averaging 124.50 citations per paper. The construction of this domain's knowledge system originates from synergistic contributions across three pioneering research trajectories: Mazzone and Elgammal (2019, cited 17 times) pioneered the exploration of AI autonomous creation through their AICAN system, establishing fundamental insights into artificial intelligence's creative boundaries and human-machine collaboration, thereby charting critical theoretical directions for subsequent research. Hong and Curran (2019, cited 249 times) conducted groundbreaking empirical work demonstrating that observer participation effectively mitigates biases against AI-generated art. Their psychological framework redefined paradigms for technology acceptance in creative domains. Rombach et al. (2022, CVPR publication with persistent citation bursts) introduced the latent diffusion model, a technical breakthrough enabling high-resolution image synthesis. This innovation catalyzed the paradigm shift of generative art from experimental exploration to industrial-scale applications. Collectively, these works establish multidimensional knowledge anchors spanning theoretical frameworks, evaluative methodologies, and technological advancements, systematically advancing the field's development. The current research frontier reveals three interconnected evolutionary trajectories: technological breakthroughs, experiential reconfiguration, and ethical reflection. Generative AI and multimodal interaction (e.g., DALL-E) are revolutionizing conventional creative paradigms through the cross-modal synthesis of text, images, and dynamic art. This evolution expands creative boundaries from static outputs to spatiotemporal continuums, facilitating unprecedented artistic exploration across both temporal and spatial dimensions. Immersive technological convergence, particularly through the integration of VR/AR and the metaverse, is redefining art perception via AI-driven interactive installations. Techniques such as neural style transfer combined with real-time rendering transform audiences from passive observers into active co-creators, fostering a bidirectional empowerment model that underpins the emerging experiential economy. Ethical and human-centric frameworks address technological risks through interdisciplinary governance. Critical issues include ambiguous copyright attribution, as exemplified by landmark cases like "The First AI Art Copyright Dispute," and algorithmic biases that perpetuate cultural misrepresentation, such as cultural appropriation or inappropriate expression. Academic efforts now prioritize synergistic governance that integrates technological, cultural, and legal perspectives to ensure that innovation aligns with humanistic principles. Collectively, these trajectories signify a paradigm shift from "tool-enabled enhancement" to "ecosystem reconfiguration," establishing a robust framework for reevaluating artistic value in the age of intelligent technologies. In summary, the integration of artificial intelligence (AI) with visual art signifies both an inevitable trend of technological empowerment and a complex practice of cultural innovation. This study employs bibliometric analysis to systematically delineate research trends in AI-driven visual art, thereby offering data-driven insights for future scholarship. Additionally, the findings serve as actionable references for policymakers, artists, and technology developers aiming to enhance the synergy between AI and visual art. On a practical level, data-informed conclusions outline three pathways: (1) Policymakers should establish ethical guidelines for the commercialization of AI art and copyright attribution through algorithmic contribution weighting, ensuring equitable recognition for both human creators and algorithmic inputs; (2) Artists can leverage diffusion models and multimodal interaction to transcend traditional media constraints; (3) Technology developers must prioritize interpretability tools to reduce creative barriers while maintaining human-centric values. This closed-loop framework, encompassing both theory and practice, provides a comprehensive guide for collaboration between AI and visual art across cognitive and operational dimensions. Future research should advance breakthroughs in technological iteration, interdisciplinary integration, ethical governance, and methodological innovation. From a technological perspective, the development of interpretable generative models based on attention mechanisms could mitigate AI's encroachment on creative sovereignty. Additionally, integrating brain-computer interfaces and biosensing technologies may facilitate bidirectional real-time art systems that respond to emotional algorithms. Interdisciplinary efforts should prioritize the preservation of digital cultural heritage, exemplified by multispectral reconstruction and generative restoration of Dunhuang murals, as well as the exploration of neuroaesthetic mechanisms, such as the correlations between fMRI-quantified dopamine release and aesthetic preferences in response to AI-generated art. Ethically, there is an urgent need for a multimodal evaluation system that incorporates metrics of cultural diversity, including non-Western style representation, and social inclusivity, particularly regarding accessibility for disabled communities. Methodologically, hybrid approaches that combine topic modeling with artist oral histories can effectively bridge cognitive gaps between technologists and creators. Furthermore, multilingual database analyses, such as those conducted on Scopus and CNKI, can elucidate global acceptance patterns and the cultural-contextual modulation of technology adoption. Collectively, these proposed directions offer systematic, cross-disciplinary solutions for reconfiguring art ecosystems in the intelligent age. 7. Limitations Several limitations still remain in this study. First, the analysis was conducted using CiteSpace and other software tools on raw data extracted exclusively from the Web of Science, with a primary focus on English-language publications. Due to technical constraints, we were unable to evaluate non-English literature or studies in other languages simultaneously. Furthermore, the employed software tools were unable to comprehensively collect all author-related data, reconcile name variations across formats for individual authors, or assess the methodological accuracy of the cited research in this field. The current analysis specifically examines research trends and focal points in artificial intelligence applications within the visual arts over the past 24 years. We intend to continue this longitudinal investigation beyond the publication of this report to track temporal developments. Additionally, our exclusive reliance on the Web of Science database has omitted potentially relevant publications from other significant repositories, including Google Scholar, ProQuest, SpringerLink, and CNKI. Future studies should broaden data sources to facilitate more comprehensive analyses and better capture the evolving dynamics of the field. Lastly, the predominant use of keyword co-occurrence analysis may have overlooked important but low-frequency research themes. For instance, the term "artificial intelligence" encompasses diverse research directions that our methodology might not fully distinguish. Subsequent investigations could enhance analytical rigor by incorporating topic model approaches to improve thematic comprehensiveness. Declarations Ethical Approval: This article does not contain any studies with human participants performed by any of the authors. Informed Consent: This article does not contain any studies with human participants performed by any of the authors. Acknowledgments : Conflict of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contribution ZY: Formal analysis, investigation, visualization, Writing original draft. 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Int J Multidisciplinary Stud Art Technol 6(1):73–104 Jiang HH, Brown L, Cheng J et al (eds) (2023) AI Art and its Impact on Artists. Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 04 Mar, 2026 Reviewers agreed at journal 22 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviews received at journal 19 Feb, 2026 Reviewers agreed at journal 14 Feb, 2026 Reviewers invited by journal 09 Feb, 2026 Editor invited by journal 04 Feb, 2026 Editor assigned by journal 24 Nov, 2025 Submission checks completed at journal 05 Nov, 2025 First submitted to journal 05 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7766646","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":588366390,"identity":"0967ddb0-64b1-4464-914b-96af9a08456d","order_by":0,"name":"ZHUOFAN YANG","email":"","orcid":"","institution":"Silla University","correspondingAuthor":false,"prefix":"","firstName":"ZHUOFAN","middleName":"","lastName":"YANG","suffix":""},{"id":588366392,"identity":"35afbba7-d842-4be1-b958-4f8c7e65c3a4","order_by":1,"name":"SHUAI YANG","email":"","orcid":"","institution":"Hangzhou Normal University","correspondingAuthor":false,"prefix":"","firstName":"SHUAI","middleName":"","lastName":"YANG","suffix":""},{"id":588366394,"identity":"f76dfe8e-36c8-4fc9-af97-0f7cf85778c9","order_by":2,"name":"JIAXINHUA WANG","email":"","orcid":"","institution":"Hangzhou Normal University","correspondingAuthor":false,"prefix":"","firstName":"JIAXINHUA","middleName":"","lastName":"WANG","suffix":""},{"id":588366395,"identity":"9988bb09-ec4b-445c-9929-4861d6a55054","order_by":3,"name":"QITAO WU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYJCCA0Asx8DM3HgAzOUhRgtQqTEDM2MD8VpA1iQ2MBCrxeBG8sbDH2ps0ue3A7X8YLCTZ+A5+4CAlrSCAweOpeVuOMzYcLCHIdmwgbfdAK8Wsxs5BgcOsB3O3QDyCw8DcwIDPxt+h0G0/DucLt8MtOUPQz2RWg62HU5gADrsMA8DkMHbhl+L/ZlnBQfO9qUZgvxyWMbguGEbzzH8WiTbkzd/qPhmIy/ff/jgwzcV1fL8PGn4tQCBASqbgE8wtIyCUTAKRsEowAIAST1KLois0koAAAAASUVORK5CYII=","orcid":"","institution":"Silla University","correspondingAuthor":true,"prefix":"","firstName":"QITAO","middleName":"","lastName":"WU","suffix":""}],"badges":[],"createdAt":"2025-10-02 13:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7766646/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7766646/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102389842,"identity":"103f4a35-3599-4eb1-8383-68397d3cd33d","added_by":"auto","created_at":"2026-02-11 08:36:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":152604,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of data retrieval and processing\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7766646/v1/a7c15db57f27dab324f8388a.png"},{"id":102389846,"identity":"805f52be-7601-4c64-a518-78c491e4aaf2","added_by":"auto","created_at":"2026-02-11 08:36:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":134935,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual publication trend analysis chart\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7766646/v1/ad12fe53a59355b07315ccb4.png"},{"id":102389847,"identity":"b8df8f72-6dc4-4c36-a4b1-d8fc3fc39b3d","added_by":"auto","created_at":"2026-02-11 08:36:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":513902,"visible":true,"origin":"","legend":"\u003cp\u003eJournal knowledge flow network analysis diagram\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7766646/v1/657a5ae00d58c73dc51d7dc4.png"},{"id":102389839,"identity":"530986fa-937f-4383-a53d-f518f4ed756a","added_by":"auto","created_at":"2026-02-11 08:36:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":206873,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal inter-country AI visual arts cooperation network\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7766646/v1/90757109544ea1f3eb9146ee.png"},{"id":102398314,"identity":"d98c62a5-4963-410e-b6b3-f3ed20c285ce","added_by":"auto","created_at":"2026-02-11 10:22:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":145962,"visible":true,"origin":"","legend":"\u003cp\u003eAuthor collaboration network diagram\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7766646/v1/ed4af9b76bc3b97ae444b9a6.png"},{"id":102389840,"identity":"f5989541-8d0e-4cbf-89d9-520a708fed40","added_by":"auto","created_at":"2026-02-11 08:36:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":233848,"visible":true,"origin":"","legend":"\u003cp\u003eInstitutional cooperation network diagram\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7766646/v1/bb90d46dc736ceb108033af6.png"},{"id":102397508,"identity":"5a78bba5-b45c-4b80-8df6-72d02db6e93f","added_by":"auto","created_at":"2026-02-11 10:17:34","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":256455,"visible":true,"origin":"","legend":"\u003cp\u003eCo-citations of references\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7766646/v1/b260ba8bc248cf0676f077f1.png"},{"id":102389845,"identity":"aabd5e88-8599-48db-ad0f-d5ad9cb0cdf2","added_by":"auto","created_at":"2026-02-11 08:36:07","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":211072,"visible":true,"origin":"","legend":"\u003cp\u003eReference clustering diagram illustrates the co-citation relationship\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7766646/v1/d44daccfd67b88f3a96b76b4.png"},{"id":102389837,"identity":"ce12713a-c21f-4629-a092-af5a4773b044","added_by":"auto","created_at":"2026-02-11 08:36:07","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":58107,"visible":true,"origin":"","legend":"\u003cp\u003eReference burst map exhibits indicates the reference’s h influence from 2001 to 2024\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7766646/v1/33241d9e2828548652eacdd5.png"},{"id":102397813,"identity":"2fc6cc37-f888-4a41-a61b-8b28e32678e1","added_by":"auto","created_at":"2026-02-11 10:19:49","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1318275,"visible":true,"origin":"","legend":"\u003cp\u003eKeyword co-occurrence network diagram\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-7766646/v1/8c9a9bbb1ab5791674326d29.png"},{"id":102389841,"identity":"5b75db26-c429-4fe0-8884-1c66240c8af3","added_by":"auto","created_at":"2026-02-11 08:36:07","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":775623,"visible":true,"origin":"","legend":"\u003cp\u003eTime evolution of keywords\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-7766646/v1/6b2546fbfd52cbec5fb563ab.png"},{"id":102398317,"identity":"5f8c82c4-b5ea-4bdf-a53e-484f21c10d3f","added_by":"auto","created_at":"2026-02-11 10:22:06","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":208631,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive theoretical knowledge framework\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-7766646/v1/6c5402d243a4b1851ce2a53c.png"},{"id":102399979,"identity":"597d1667-6125-4d17-867b-f95243bfa5c8","added_by":"auto","created_at":"2026-02-11 10:37:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4962567,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7766646/v1/10a42806-39b7-4ac5-97dc-008e37719519.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Innovations in Visual Arts Driven by Artificial Intelligence: Global Research Hotspots and Emerging Trends","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRecently, the application of artificial intelligence (AI) technology in the arts driving revolutionary changes in artistic creation, art markets, and cultural exchange[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. AI not only enhances traditional art forms through generative approaches but also offers unique research perspectives for cultural integration, art project creation, and environmental art design[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This technology has played a crucial role in promoting artistic creation, facilitating cultural exchange, and expanding the boundaries of the art market[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, the application of AI in artistic creation has altered the creative processes, enabling the interaction and inspiration between AI and human artists[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the controversies remain in the originality of artistic works, the cultural identity, and the balance between technological innovation and human creativity[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMeanwhile, technologies such as generative adversarial networks (GANs) have emerged as a creative tool for artistic creation, facilitating style transfer and generating new art models[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The rapid advancement of generative models, alongside the deep application of AI, makes the cognitive feedback and emotional experience of the public at the risk of being marginalized[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This imbalance between technology and humanistic perspective may bring profound challenges to the mechanism of human-AI collaborative creation[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the expansion of artificial intelligence (AI) research in the arts, the development of its knowledge framework has not kept up with technological advancements[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The rapid accumulation of literature in this field exposed significant challenges in academic integration: the significance of basic literature remains undefined, collaborative networks among key academic communities require clarification, and systematic tracking of knowledge evolution and paradigm shifts is absent[\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In this context, bibliometrics emerges as a vital tool for quantitatively analyzing disciplinary development, especially in studies involving AI and visual arts[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Through techniques such as citation network analysis and co-occurrence clustering, bibliometrics facilitates the precise identification of domain knowledge structures, dynamic tracking of academic communities, and localization of key knowledge nodes, thereby offering data-driven insights into disciplinary trajectories[\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. For interdisciplinary fields, bibliometric analysis helps to overcome the subjective limitations of traditional literature reviews by revealing hidden research pathways through objective data and explain the dissemination mechanisms of core literature[\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on this, our study employs bibliometric methods for system analysis of 379 peer-reviewed articles indexed in the Web of Science Core Collection from 2001 to 2024, focusing on AI applications in the visual arts. The research aims to elucidate the core issues, evolutionary trends, and future directions within this field. The following research questions (RQs) will be discussed in this investigation:\u003c/p\u003e \u003cp\u003eRQ1: What are the research trends and major journal distributions in AI-driven visual arts studies over the past 24 years?\u003c/p\u003e \u003cp\u003eRQ2: Which nations, institutions, and researchers emerge as the predominant contributors in this disciplinary domain?\u003c/p\u003e \u003cp\u003eRQ3: What constitutes the foundational knowledge base and seminal literature in AI-visual arts research?\u003c/p\u003e \u003cp\u003eRQ4: What are the current research hotspots and developmental trajectories in AI-enabled visual arts?\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Sources and Search Strategy\u003c/h2\u003e \u003cp\u003eThe data for this study were sourced from the Web of Science (WOS) Core Collection, a globally recognized and authoritative platform for academic literature retrieval and analysis, developed and maintained by Clarivate Analytics[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The WOS Core Collection indexes high-quality journals across various disciplines, including natural sciences, social sciences, humanities, and the arts, ensuring the scientific validity and representativeness of the analysis through its comprehensive coverage and high reliability[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. A thematic search strategy was devised to intersect \"Artificial Intelligence\" and \"Visual Arts\" using the following query: TS=(\"Artificial Intelligence\" OR \"AI\") AND TS=(\"Visual Arts\" OR \"Digital Art\" OR \"Generative Art\" OR \"Media Art\" OR \"Interactive Art\" OR \"Computer-Generated Art\" OR \"AI-Generated Art\" OR \"Creative Design\" OR \"3D Art\" OR \"Graphic Design\" OR \"Aesthetic Experience\" OR \"Installation Art\" OR \"New Media Art\" OR \"Algorithmic Art\" OR \"Painting\" OR \"Sculpture\").The search covered publications from 2001 to 2024, yielding an initial corpus of 698 articles. Document types were restricted to \"Article\" and \"Review,\" and the language was limited to English. After applying these filters, 319 irrelevant entries were excluded based on predefined criteria: News Item (n\u0026thinsp;=\u0026thinsp;1), Meeting Abstracts (n\u0026thinsp;=\u0026thinsp;2), Retraction Notices (n\u0026thinsp;=\u0026thinsp;3), Book Chapters (n\u0026thinsp;=\u0026thinsp;5), Editorial Material (n\u0026thinsp;=\u0026thinsp;10), Retracted Publications (n\u0026thinsp;=\u0026thinsp;16), Early Access articles (n\u0026thinsp;=\u0026thinsp;36), Proceeding Papers (n\u0026thinsp;=\u0026thinsp;222), and Non-English publications (n\u0026thinsp;=\u0026thinsp;24). The final dataset comprised 379 publications, including 362 research articles and 17 review papers. The data retrieval and processing workflow is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research Method\u003c/h2\u003e \u003cp\u003eBibliometrics is a mathematical and statistical approach that quantitatively analyzes scientific literature, focusing on its production, dissemination, and impact[\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. As a key branch of scientometrics, this method reveals the knowledge structure, research hotspots, developmental trends, and patterns of scientific communication within specific fields[\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. By examining metrics such as publication volume, authorship, institutional contributions, keywords, journal distribution, and citation networks, bibliometrics offers a systematic overview of scientific research, with applications in academic research, scientific policy formulation, and disciplinary evaluation[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study utilized Microsoft Excel 2021 to analyze publication trends from 2001 to 2024. VOSviewer 1.6.18 and Scimago Graphica were employed to investigate co-occurrence and temporal variations among countries, institutions, authors, and keywords. In the generated visualizations, nodes are represented as spheres with text labels, where sphere size corresponds to node significance. Distinct colors indicate different clusters, while connecting lines illustrate co-occurrence relationships, with line thickness reflecting the strength of these relationships.\u003c/p\u003e \u003cp\u003eCiteSpace 6.1.R1 (developed by Chaomei Chen, China) was used to visualize journal dual-overlay and document co-citation analyses. For co-citation mapping, CiteSpace parameters were configured as follows: time slices spanning 2001 to 2024, a slice length of one year, and selection criteria (k\u0026thinsp;=\u0026thinsp;25). In the co-citation network, spheres represent cited references, with size proportional to citation frequency. Connections between spheres denote co-citation relationships. The concentric rings within each sphere illustrate the number of citations and their corresponding time periods, with ring size and color indicating citation volume and temporal distribution, respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The distributions, relationships and contributions of journals in the visual arts field\u003c/h2\u003e \u003cp\u003eThe analysis of annual publication output trends provides critical insights into the development of research field and the hotspots of academic attention and serves as a foundation for understanding the emergence, expansion, and potential research directions in the future [\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. We illustrate the annual publication output in this field from 2001 to 2024, result shows that the field has progressed through distinct phases, transforming from slow growth to rapid expansion \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBetween 2001 and 2014, the annual publication output remained relatively low, with only 1 to 3 publications created per year. A marked increase occurred after 2015, with exponential growth observed between 2018 and 2024, rising from 17 publications in 2018 to 103 in 2024. This surge underscores accelerated advancements in the field, indicating its transition into an active research phase. This growth pattern aligns closely with the widespread adoption of artificial intelligence technologies and their deepening integration into interdisciplinary applications.\u003c/p\u003e \u003cp\u003eCurve-fitting analysis reveals a strong correlation between publication trends and a quartic polynomial model (equation: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{y}=0.0191{\\varvec{x}}^{4}-0.5706{\\varvec{x}}^{3}+5.6318{\\varvec{x}}^{2}-18.747\\varvec{x}+20.167\\)\u003c/span\u003e\u003c/span\u003e), achieving an exceptional goodness-of-fit (R\u0026sup2; = 0.9981), which demonstrates robust development in this field. Current analysis suggests an annual volume will continue to rise in the coming years, which is closely related to the popularity of artificial intelligence technology and its in-depth application in interdisciplinary fields.\u003c/p\u003e \u003cp\u003eAccording to the Web of Science classifications, the 379 publications are distributed in 109 different categories. We list the top 20 discipline categories by publication volume (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among 41 publications, the most prolific category is Humanities Multidisciplinary, followed closely by Art with 40 publications, Computer Science Information Systems with 38 publications, Computer Science Artificial Intelligence with 33 publications, and Engineering Electrical Electronic with 31 publications. These high-productivity disciplines reflect the extensive attention and in-depth exploration of this interdisciplinary research theme within the global academic community. Moreover, the adoption of interdisciplinary methodologies has provided diverse perspectives and a robust theoretical foundation for the application of artificial intelligence in visual arts fields[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. This multidisciplinary integration not only fosters innovation within the field but also opens new directions for future research[\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 20 disciplines by number of publications\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeb of Science Categories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeb of Science Categories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHumanities Multidisciplinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChemistry Multidisciplinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComputer Science Interdisciplinary Applications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComputer Science Information Systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEducation Educational Research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComputer Science Artificial Intelligence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChemistry Analytical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEngineering Electrical Electronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePhysics Applied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaterials Science Multidisciplinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNeurosciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTelecommunications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComputer Science Theory Methods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComputer Science Software Engineering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePhilosophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultidisciplinary Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePsychology Multidisciplinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEngineering Multidisciplinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpectroscopy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNP: Number of Publications\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNext, we sought to utilized the dual-map overlay, which is a visualization method that integrates maps generated by CiteSpace, providing an intuitive illustration of interdisciplinary journal relationships [\u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. This approach effectively highlights the connections between citing fields (on the left) and cited fields (on the right), thereby revealing knowledge flow across disciplines at the journal level. For the clustering analysis, the built-in Z-score algorithm in the software was employed to process the data. The results demonstrate distinct clusters that reflect the directional flow of disciplinary knowledge, the citation relationships between citing journals (left) and cited journals (right) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The former represents applied domains, while the latter reflect disciplinary foundations. Colored connecting lines denote citation pathways between journals in distinct fields, highlighting publication and citation activities across these domains. Key citation trajectories originate from 'SYSTEMS, COMPUTING, COMPUTER' and 'PSYCHOLOGY, EDUCATION, SOCIAL,' extending toward emerging research frontiers such as 'MATHEMATICS, SYSTEMS, MATHEMATICAL' and 'PSYCHOLOGY, EDUCATION, HEALTH,' respectively. Notably, the citation pathway from 'SYSTEMS, COMPUTING, COMPUTER' to 'MATHEMATICS, SYSTEMS, MATHEMATICAL' exhibits the highest Z-score (z\u0026thinsp;=\u0026thinsp;5.12), underscoring its critical role in knowledge dissemination and interdisciplinary influence.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe also generated the top ten journals ranked by publication citation frequency \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. And Applied Sciences - Basel from Switzerland (Impact Factor IF\u0026thinsp;=\u0026thinsp;2.5) has the highest number of published papers, with a total of 11 papers, followed by Artnodes (IF\u0026thinsp;=\u0026thinsp;0.2) from Spain and the Journal of Cultural Heritage (IF\u0026thinsp;=\u0026thinsp;3.5) from France, which published 10 and 9 articles, respectively. These findings indicate the prominent role of these journals within the research field and their significant contributions to advancing knowledge in this domain. Notably, the Journal of Cultural Heritage (IF\u0026thinsp;=\u0026thinsp;3.5) from France boasts the highest impact factor among the ten journals listed. The right panel of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e highlights the top ten journals and conferences ranked by citation count. ArXiv ranks first with 363 citations, followed by the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and Lecture Notes in Computer Science (LNCS), with 224 and 166 citations, respectively. This illustrates the substantial academic influence of these journals and conferences, reflecting their pivotal role in knowledge dissemination and the advancement of the field.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 journals or conferences by number of publications and citations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJournals\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIF(JCR2023)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCited journals or meetings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIF\u003c/p\u003e \u003cp\u003e(JCR2023)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAPPLIED SCIENCES-BASEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSwitzerland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eARXIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\\\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\\\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eARTNODES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePROC CVPR IEEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\\\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\\\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJOURNAL OF CULTURAL HERITAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLECT NOTES COMPUT SC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\\\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\\\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIEEE ACCESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIEEE ACCESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSUSTAINABILITY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSwitzerland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCOMPUT HUM BEHAV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eADV NEUR IN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\\\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\\\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHELIYON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePLOS ONE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI \u0026amp; SOCIETY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLEONARDO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eARTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSwitzerland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIEEE I CONF COMP VIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\\\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\\\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJOURNAL OF SCIENCE AND TECHNOLOGY OF THE ARTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFRONT PSYCHOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSwitzerland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn conclusion, the above analysis illustrates that the publications in visual arts field exhibit rapid expansion, while closely related to \u0026ldquo;MATHEMATICS, SYSTEMS, MATHEMATICAL\u0026rdquo; and published by different kinds of journals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 National, individual and institutional scholarly contributions\u003c/h2\u003e \u003cp\u003eAnalyzing collaborations among countries and regions reveals valuable insights into research cooperation patterns and intensity, which reflect scientific capabilities, resource-sharing practices, and international influence within a given field[\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. This study encompasses contributions from 55 countries in terms of scholarly outputs and list the top ten countries ranked by publication volume in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Among them, China has the largest number of publications with a total of 137 papers, followed by the United States with 64 of them. The United Kingdom exhibits the highest average citations per publication, followed by Austria and the United States, indicating a relatively higher research quality in these nations. In contrast, Russia and South Korea display lower average citations per publication, suggesting potential improvement of their academic outputs. Furthermore, China and the United States achieve the highest H-index scores, indicating their dominant scholarly influence in this research domain.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 countries by number of publications\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH-index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRussia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA visual analysis of the collaborative relationships among countries and regions was conducted using VOSviewer software \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The analysis was configured with a minimum threshold of five publications per country, based on this we generating a collaborative network map that reveals the distribution and intensity of international partnerships. In the visualization, the size of the nodes corresponds to national publication output, with larger circles indicating higher productivity. The thickness of the lines reflects the strength of bilateral collaboration, where thicker connections denote closer cooperation, while the gradient color of the lines signifies the overall collaborative intensity between a country and its partners. The connecting lines highlight China as the most prominent country for international collaboration, demonstrating robust partnerships with multiple nations, particularly the United States, Australia, and the United Kingdom. The United States emerge as the secondary nodal center, with significant collaborative ties to both China and the UK, underscoring its pivotal role in global academic networks. In contrast, several countries, including several European nations and Russia, exhibit comparatively weaker collaborative linkages, suggesting opportunities for enhanced integration into international scholarly networks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis of author contributions reveals collaborative relationships among scholars and the structures of their academic networks, thereby identifying core researchers who play leading roles in specific research domains. In this field, a total of 1,144 authors participated in the relevant studies. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the top ten authors ranked by publication count. Among these authors, Lin Rungtai and Lyu Yanru had the highest number of publications, each publishing four articles. Other prolific contributors included Goel Ashok K. and Grba Dejan, each publishing three articles. Cetinic Eva achieved the highest average citation count per paper, indicating the superior academic quality of their work and its recognition within the field. Additionally, Lin Rungtai and Lyu Yanru exhibited the highest H-index, further underscoring their scholarly influence in this domain.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 authors by number of publications\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH-index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLin, Rungtai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLyu, Yanru\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGoel, Ashok K.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrba, Dejan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCetinic, Eva\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAltenburger, P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBusse, Hj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eK\u0026auml;mpfer, P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLubitz, W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchumann, P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis study utilized VOSviewer software to perform a visualization analysis of the collaborative relationships among authors. By establishing a minimum publication threshold of two articles per author, a collaborative network map comprising 37 authors was generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In this visualization, nodes represent individual authors, while the connecting lines signify collaborative partnerships. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the author collaboration network presents both closely-connected core research teams and peripheral loosely-cooperating groups. The most prominent cluster (in red) centers around Lubitz, W., and Schumann, P., demonstrating strong academic cohesion and disciplinary dominance through intensive intra-team collaboration. A secondary collaborative network emerges around scholars such as Kupczyk, Erwin, and Mueller, Constanze, which exhibits notable collaborative research tendencies, though with lower interaction density compared to the red cluster. Meanwhile, independent researchers like Cetinic, Eva, and Spjuth, Ola appear with limited collaborative ties to other scholars, reflecting their relatively limited academic influence. These patterns indicate substantial potential for expanding and optimizing collaborative networks within this research domain.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInstitutional analysis reveals the research landscape and key contributors within a field, providing scholars with insights into the academic influence and collaborative networks of prominent institutions[\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. This analysis also serves as a reference for future research planning and cross-institutional collaboration. In the artificial intelligence (AI) applications in visual arts research, a total of 593 institutions have participated in relevant studies. The top ten institutions ranked by publication volume are listed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, illustrating the distribution of research and the academic impact of these organizations. The Hong Kong University of Science and Technology (HKUST) leads with six published papers, achieving a total citation count of 132 and an average of 22.00 citations per paper, significantly surpassing other institutions. This underscores the substantial academic influence and international recognition of HKUST\u0026rsquo;s research in this field. Zhejiang University follows closely with five publications, accumulating a total of 57 citations and an average of 11.40 citations per paper. Institutions such as Macau University of Science and Technology, the Chinese Academy of Sciences, and Nanyang Technological University also contributed five publications each, demonstrating the active engagement of institutions from Mainland China and Singapore in this domain. Notably, the Georgia Institute of Technology, despite its relatively lower publication count, achieved a total citation count of 110 and the highest average citations per paper of 27.50 among all institutions. This highlights the exceptional depth and broad academic impact of its research contributions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 institutions by number of publications\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganization\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH-index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHONG KONG UNIV SCI \u0026amp; TECHNOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZHEJIANG UNIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMACAU UNIV SCI \u0026amp; TECHNOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHINESE ACAD SCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNANYANG TECHNOL UNIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGEORGIA INST TECHNOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBEIJING TECHNOL \u0026amp; BUSINESS UNIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNATL TAIWAN UNIV ARTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSHANGHAI JIAO TONG UNIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWUHAN UNIV TECHNOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis study utilized VOSviewer to visualize the collaborative relationships among institutions. By establishing a minimum publication threshold of two papers per institution, a collaborative network consisting of 78 institutions was generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In the network diagram, nodes represent institutions, while connecting lines indicate collaborative partnerships. The analysis reveals that Shanghai Jiao Tong University, Zhejiang University, and the Hong Kong University of Science and Technology (HKUST) constitute a central collaborative hub, highlighting China\u0026rsquo;s strong research capabilities in this field. Moreover, institutions such as Nanyang Technological University, Stanford University, and the University of Cambridge act as critical nodes within international collaboration networks, playing a significant role in advancing the field. Looking ahead, enhancing global collaborative networks\u0026mdash;especially by fostering connections between regional research clusters\u0026mdash;could effectively promote synergistic innovation and sustained development in this area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 The evolution of hotspots in visual arts\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1) Co-citation analysis\u003c/h3\u003e\n\u003cp\u003eCo-citation analysis allows scholars to uncover latent relationships, emerging themes, and the evolution of disciplines within a knowledge domain[\u003cspan additionalcitationids=\"CR69\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. A co-citation relationship is established when two publications (A and B) are cited together by a third publication (C). In this study, we utilized a g-index (k\u0026thinsp;=\u0026thinsp;25) to extract 524 references from the 379 included articles. Subsequent co-citation and clustering analyses were performed to delineate the core intellectual framework and trace the evolution of research hotspots in this field.\u003c/p\u003e \u003cp\u003eAfter analysis, we present the co-citation network of references, highlighting the 10 most frequently cited works (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Each node is labeled with the first author\u0026rsquo;s name and publication year. The largest node in the network corresponds to the article \"Art, Creativity, and the Potential of Artificial Intelligence\" by Mazzone and Marian et al. (2019), published in ARTS, which holds the highest citation count (17 citations). This underscores its significant influence within the research domain. The study focuses on the artificial intelligence art creation system \"AICAN,\" examining the application of AI in 21st-century artistic practices and its implications for redefining art and artists. The authors argue that AICAN\u0026rsquo;s outputs should be recognized as legitimate artworks and advocate for collaborative frameworks between human and machine creativity to fully harness their combined potential.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBuilding on this analysis, the study further conducts a cluster network analysis of the references. Using the log-likelihood ratio (LLR) algorithm, cluster labels were extracted from title fields, identifying five major clusters among the 524 references. The clustering results indicate a Q-value of 0.9321 (exceeding the threshold of 0.3), confirming a robust cluster structure, and an S-value of 0.9215 (above 0.7), demonstrating high reliability. The cluster network comprises the following five key themes: #0 \"creating art,\" #1 \"AI-based painting system,\" #2 \"artificial intelligence,\" #7 \"critical inquiry,\" and #17 \"co-creative drawing system\"\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe phenomenon of citation bursts reflects rapidly emerging research foci within specific time periods[\u003cspan additionalcitationids=\"CR72\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. A higher burst value indicates that a reference has exerted greater influence during its active timeframe. This metric serves as a valuable tool for identifying research frontiers and tracing the evolutionary trajectory of prominent thematic trends within a discipline[\u003cspan additionalcitationids=\"CR75\" citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e illustrates the citation bursts identified in our analysis. Among the references with high burst strength that remain influential today, a representative example is \"Putting the Art in Artificial: Aesthetic Responses to Computer-Generated Art.\" This study investigates public evaluations of visual artworks created by humans versus those generated by artificial intelligence (AI). The findings reveal a persistent bias against computer-generated art, with participants attributing lower aesthetic value to such works. However, this bias diminishes when observers witness the robotic creation process, as the act of observation fosters anthropomorphic attributions to the computational system. Another notable work exhibiting a strong burst is \"Artificial Intelligence, Artists, and Art: Attitudes Toward Artwork Produced by Humans vs. Artificial Intelligence.\" This research explores public perceptions of AI-generated artworks and examines how the artist\u0026rsquo;s identity (human or AI) influences evaluations of artistic merit. The study demonstrates that while distinctions exist in the perceived artistic value of human versus AI-created works, the attribution of authorship to AI does not result in a statistically significant reduction in overall artistic appraisal.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e2) Co-occurrence Evolution\u003c/h3\u003e\n\u003cp\u003eCo-occurrence analysis examines the relationships between frequently occurring keywords and terms in scholarly literature[\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. This method seeks to delineate the thematic structure of a research field, identify connections among studies, synthesize historical research trends, and predict possible future research directions [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study conducted a keyword clustering analysis using VOSviewer software. By establishing a minimum keywords occurrence threshold of three, 119 keywords were selected to construct a keyword co-occurrence visualization map (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The map comprises nodes represented by circles and labels, where the size of each node corresponds to the frequency of the keywords, and the thickness of the connecting lines reflects the strength of the relationships between keywords. The colors of the nodes denote distinct research domains, forming five primary clusters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCluster #1: Technical Foundations of Deep Learning and Computer Vision\u003c/p\u003e \u003cp\u003eThis cluster focuses on key technical terms such as \"deep learning,\" \"machine learning,\" and \"convolutional neural networks (CNNs).\" It emphasizes the crucial role of deep learning in AI-driven visual art applications, specifically in image generation, classification, and style transfer. Techniques such as \"style transfer\" and \"generative adversarial networks (GANs)\" are particularly significant in art generation and digital image processing. Additionally, keywords associated with \"paintings\" suggest a research emphasis on the digitization of traditional artistic styles and the creative reconstruction enabled by deep learning.\u003c/p\u003e \u003cp\u003eCluster #2: Computational Creativity and Generative Art\u003c/p\u003e \u003cp\u003eThis cluster emphasizes keywords such as \"generative art,\" \"creativity,\" and \"computational creativity,\" reflecting explorations into AI-driven innovation in artistic creation. Terms like \"intention\" and \"algorithmic frameworks\" underscore efforts to simulate or enhance human creative intent through computational models. Additionally, associations with \"education\" and \"robotics\" suggest the expanding applications of generative art into cross-disciplinary educational contexts and interactive creation.\u003c/p\u003e \u003cp\u003eCluster #3: Virtual Reality and Technological Expansion\u003c/p\u003e \u003cp\u003eThis cluster is anchored by the themes of \"virtual reality\" (VR) and \"technology,\" reflecting the profound integration of artificial intelligence with cutting-edge technological advancements. Keywords such as \"augmented reality,\" \"interactive art,\" and \"metaverse\" illustrate that VR and metaverse technologies are creating novel mediums and possibilities for artistic creation. Furthermore, the interconnectedness of \"natural language processing\" (NLP) and \"image processing\" highlights a broader trend in artificial intelligence toward the development of multimodal interaction systems and the design of immersive art experiences.\u003c/p\u003e \u003cp\u003eCluster #4: Holistic Applications of AI and Ethical Considerations\u003c/p\u003e \u003cp\u003eThis cluster revolves around artificial intelligence (AI) and generative artificial intelligence (GAI), encompassing keywords such as ethics, innovation, and media art. It highlights the widespread adoption of AI in visual arts, along with its societal impacts and the ethical controversies it raises. The emphasis on ethics underscores academic concerns regarding the misuse of technology, cultural conflicts, and the accountability associated with AI-generated art.\u003c/p\u003e \u003cp\u003eCluster #5: Artistic Design and User Experience\u003c/p\u003e \u003cp\u003eThis cluster revolves around keywords such as \"design,\" \"model,\" and \"interactivity,\" highlighting the application of artificial intelligence (AI) in optimizing artistic design and enhancing user experience. The prominence of terms like \"information\" and \"perception\" indicates that research efforts are concentrated on improving the interactivity and efficiency of information delivery in AI-driven artistic works. Furthermore, the inclusion of concepts such as \"sustainability\" and the \"technology acceptance model (TAM)\" emphasizes the importance of ensuring the sustainable development of AI technologies within the realm of artistic design.\u003c/p\u003e \u003cp\u003eThe further analysis expands upon the keyword clustering analysis presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e by illustrating the temporally weighted distribution of keywords \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The color gradient, ranging from dark blue to bright yellow, signifies the chronological prominence of research themes, with dark blue representing early-stage focal areas and bright yellow indicating recent trends. The analysis reveals that early research primarily concentrated on foundational technological investigations, such as 'deep learning,' 'machine learning,' and 'classification.' These keywords highlight the initial focus on applying artificial intelligence (AI) to fundamental visual art tasks, including image recognition, feature extraction, and model optimization. With the development of technology, research has gradually shifted towards higher-level applications. Keywords such as 'generative art,' 'AI art,' and 'computational creativity' emerged as central themes, marking significant advancements in AI-driven artistic creation. Notably, the adoption of generative adversarial networks (GANs) facilitated the production of AI-generated artworks, signifying a paradigm shift in creative methodologies. Since 2022, research trajectories have diversified to emphasize human-centric and experiential dimensions. Keywords like 'media art,' 'augmented reality (AR),' 'virtual reality (VR),' and 'interactivity' underscore a shift from static image generation to multisensory immersive experiences. This evolution illustrates AI's expanding role in dynamic, multidimensional artistic expression. Concurrently, terms such as 'generative AI,' 'technology,' and 'social media' highlight the increasing prominence of generative AI systems (e.g., GPT, DALL-E) as leading research topics. Recent studies not only investigate technical innovations but also explore their societal implications, including applications in public engagement, cultural discourse, and collaborative creativity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAn analysis of artificial intelligence (AI) applications in visual arts reveals a complex developmental trajectory characterized by dynamically shifting research priorities and focal topics across different periods. Emerging research directions often represent expansions or refinements of existing themes, thus, the changes in research hotspots and directions are dynamic. Although previous studies have reviewed and analyzed AI applications in visual arts, they have yet to establish a cohesive knowledge framework that allows readers to intuitively obtain key insights. Therefore, there is an urgent need to construct a novel, comprehensive, and intuitive theoretical framework for the application of artificial intelligence in visual art, providing both overview and guidance for future research \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese disciplinary categories reflect the global academic community's extensive attention and in-depth exploration of this interdisciplinary research theme. Through disciplinary analysis, we can identify entry points for artificial intelligence applications in the visual arts and their complex theoretical foundations, thereby revealing the research potential and scholarly value of the field. The collaborative network analysis reveals dynamic partnerships among geographic regions, authors, and institutions. This study identifies the leading collaborative entities within the field, providing researchers with reference points for identifying potential collaborators and suitable research domains. At the regional level, China, the United States, and the United Kingdom exhibit the highest levels of collaborative activity. Among individual researchers, Lin Rungtai, Lyu Yanru, and Ashok K. Goel stand out as the most prolific collaborators. The institutional analysis highlights three primary contributors: Hong Kong University of Science and Technology (HKUST), Zhejiang University (ZJU), and Macau University of Science and Technology (MUST). The bibliographic coupling analysis identifies key co-cited publications, including works by Mazzone and Elgammal (2019), Hong and Curran (2019), and Rombach et al. (2022). These studies not only advance the integration of artificial intelligence and art through technical innovations but also establish critical theoretical and practical frameworks that underpin the field's evolution.\u003c/p\u003e \u003cp\u003eThe development of research hotspots highlights important problems and new developments in the area, defining central themes and possible turning points at various times [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. The purpose of this analysis is to forecast subjects that are likely to become prominent, identify future research avenues, and clarify the trajectory of domain evolution. Initially, research efforts focused mostly on fundamental ideas like \"deep learning,\" \"machine learning,\" and \"classification,\" which reflected preliminary investigations into the use of artificial intelligence (AI) technology in the visual arts. In the intermediate stage, \"generative art,\" \"AI art,\" and \"computational creativity\" emerged as key themes as research focus shifted to higher-level applications. This change represented significant advances in AI-powered creative production. Diversification has become more and more important in recent trends after 2022, marked by keywords such as \"media art,\" \"augmented reality,\" \"virtual reality (VR),\" and \"interactivity.\" These developments indicate that AI's role in visual arts has expanded beyond static art generation to encompass dynamic, experiential domains. Notably, the emergence of terms like \"generative AI,\" \"technology,\" and \"social media\" underscores the growing prominence of generative AI systems (e.g., GPT and DALL-E) as cutting-edge research topics in visual arts. These insights provide valuable guidance for future scholars.\u003c/p\u003e \u003cp\u003eBy synthesizing high-frequency keywords alongside those exhibiting elevated betweenness centrality, a mapping of research hotspots was generated. Following the exclusion of foundational keywords such as 'artificial intelligence' and 'visual art,' 14 high-frequency and high-centrality keywords were identified and categorized into \u0026ldquo;Generative Art and Computational Creativity \u0026ldquo; [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e], \u0026ldquo;Virtual Reality (VR) and Augmented Reality (AR)\u0026rdquo;[\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e], \u0026ldquo;Interactive Art and Multimodal Experiences\u0026rdquo; [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e] and \u0026ldquo;Generative AI and Social Media\u0026rdquo; [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e] four thematic clusters.\u003c/p\u003e"},{"header":"5. Future Trends and challenges","content":"\u003cp\u003eArtificial intelligence exhibits substantial technological advantages in invigorating artistic production[\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. Generative Adversarial Networks (GANs) and deep learning models have reached significant milestones in image generation, as evidenced by the works of Mario Klingemann and Google's DeepDream project. Future advancements in these technologies are poised to enhance the complexity and expressiveness of creative outputs, facilitating breakthroughs in image quality and realism. This progress will enable high-fidelity restoration and innovative integration of diverse material textures and light/shadow effects, continually pushing the boundaries of artistic practice.\u003c/p\u003e \u003cp\u003eThe integration of AI and art lies in its capacity to catalyze cross-disciplinary collaboration[\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. For instance, interactive art creation necessitates expertise that spans computer science, artistic design, and psychology. Moving forward, this interdisciplinary synergy is expected to intensify, driving breakthroughs in the conceptual frameworks, formal expressions, and social value dimensions of artistic practice. Projects such as TeamLab's immersive installations and the Tangible Media Group's research exemplify how cross-domain innovation can propel the evolution of artistic disciplines.\u003c/p\u003e \u003cp\u003eAs artificial intelligence (AI) becomes increasingly prevalent in the realm of art creation, addressing ethical challenges-especially those related to copyright attribution becomes imperative [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. The rise of AI-generated artworks necessitates the establishment of legal frameworks that are comparable to those governing traditional art, ensuring clear copyright ownership and protecting the rights of stakeholders. Well-defined regulations will mitigate ambiguities that could obstruct artistic production and dissemination, paralleling the necessity for established authorship in conventional art to safeguard the interests of creators [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAI's robust data-processing capabilities empower personalized artistic creation and cultural hybridization[\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. By analyzing diverse cultural and artistic elements, AI tools can generate bespoke creative materials for artists. For instance, generative AI systems produce culturally distinctive artworks tailored to specific heritage contexts. This capability fosters cross-cultural exchange and mutual enrichment within the arts, thereby enhancing global artistic diversity\u0026mdash;similar to how artists from varied backgrounds leverage technology to synthesize unique aesthetic expressions.\u003c/p\u003e \u003cp\u003eThe application of artificial intelligence in the field of visual arts has brought new tools for education and cultural dissemination [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. Digital art platforms that utilize AI enable the rapid and large-scale distribution of artworks and cultural information, transcending geographic and linguistic barriers. For instance, VR-enhanced online art courses provide global learners with immersive experiences, thereby strengthening educational engagement and fostering intercultural dialogue[\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. Such innovations amplify the societal impact of art and facilitate shared cultural prosperity, paralleling the ways in which traditional museums leverage digital platforms to broaden their reach[\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile advancements in artificial intelligence (AI) present significant opportunities in the visual arts, they also pose multiple challenges. These challenges include issues of artistic originality, cultural interpretation, the balance between technology and creativity, and ethical concerns including \u0026ldquo;ambiguity in artistic originality and authenticity\u0026rdquo;,\u0026rdquo; cultural misinterpretation and expression biases\u0026rdquo;,\u0026rdquo; imbalance between technical execution and creative innovation \u0026ldquo;and \u0026ldquo;copyright and legal frameworks issue\u0026rdquo;\u003c/p\u003e \u003cp\u003eIn traditional art creation, the artist's unique thoughts, emotions, cultural background and creative skills are the core of the work's originality [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]. In contrast, AI - driven creation is difficult to determine whether the work stems from the mechanical generation [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e].Besides, existing copyright laws, which were originally designed for human-authored works, exhibit significant limitation when applied to AI-generated art[\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]. AI systems employed in the visual arts rely on extensive datasets that may encompass sensitive personal information or unauthorized artworks[\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. The use of non-compliant data sources or improper utilization could result in violations of privacy and intellectual property rights.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThis study employs bibliometric methods to systematically analyze research trends, knowledge structures, and developmental trajectories in the application of artificial intelligence (AI) to visual arts from 2001 to 2024. By integrating 379 publications from the Web of Science Core Collection and utilizing tools such as CiteSpace and VOSviewer, we elucidate the field's research hotspots, international collaboration networks, core knowledge bases, and emerging frontiers. Over the past 24 years, research on artificial intelligence in visual arts has evolved from a phase of gradual exploration (2001\u0026ndash;2014, with an annual average of 1\u0026ndash;3 publications) to exponential growth (2018\u0026ndash;2024, with annual outputs ranging from 17 to 103 publications). This trajectory closely aligns with technological breakthroughs in generative models\u0026mdash;such as GANs and diffusion models\u0026mdash;and their cross-disciplinary integration. A quartic polynomial fitting model (R\u0026sup2; = 0.9981) predicts sustained research activity in the coming years.\u003c/p\u003e \u003cp\u003eA multi-tiered analysis encompassing national, institutional, and individual scholarly contributions reveals distinct geographic and collaborative dynamics in AI-driven visual arts research. China, with 137 publications, and the United States, with 64 publications, dominate the global output in terms of sheer volume. Meanwhile, the United Kingdom stands out as a benchmark for academic influence, boasting an average of 14.13 citations per paper. At the institutional level, the Hong Kong University of Science and Technology, with 6 publications and a total of 132 citations, along with Zhejiang University, which has 5 publications and 57 total citations, emphasizes interdisciplinary technical-artistic exploration. Conversely, the Georgia Institute of Technology demonstrates exceptional impact per paper, with an average of 27.50 citations. The international collaboration network underscores China\u0026rsquo;s pivotal role as a hub, facilitating cross-border knowledge exchange through robust partnerships with the U.S. and Australia. At the individual level, Lin Rungtai, with 4 publications, drives applied innovation through intensive collaboration, while Cetinic Eva establishes theoretical foundations for the field through high-impact publications, averaging 124.50 citations per paper.\u003c/p\u003e \u003cp\u003eThe construction of this domain's knowledge system originates from synergistic contributions across three pioneering research trajectories: Mazzone and Elgammal (2019, cited 17 times) pioneered the exploration of AI autonomous creation through their AICAN system, establishing fundamental insights into artificial intelligence's creative boundaries and human-machine collaboration, thereby charting critical theoretical directions for subsequent research. Hong and Curran (2019, cited 249 times) conducted groundbreaking empirical work demonstrating that observer participation effectively mitigates biases against AI-generated art. Their psychological framework redefined paradigms for technology acceptance in creative domains. Rombach et al. (2022, CVPR publication with persistent citation bursts) introduced the latent diffusion model, a technical breakthrough enabling high-resolution image synthesis. This innovation catalyzed the paradigm shift of generative art from experimental exploration to industrial-scale applications. Collectively, these works establish multidimensional knowledge anchors spanning theoretical frameworks, evaluative methodologies, and technological advancements, systematically advancing the field's development.\u003c/p\u003e \u003cp\u003eThe current research frontier reveals three interconnected evolutionary trajectories: technological breakthroughs, experiential reconfiguration, and ethical reflection. Generative AI and multimodal interaction (e.g., DALL-E) are revolutionizing conventional creative paradigms through the cross-modal synthesis of text, images, and dynamic art. This evolution expands creative boundaries from static outputs to spatiotemporal continuums, facilitating unprecedented artistic exploration across both temporal and spatial dimensions. Immersive technological convergence, particularly through the integration of VR/AR and the metaverse, is redefining art perception via AI-driven interactive installations. Techniques such as neural style transfer combined with real-time rendering transform audiences from passive observers into active co-creators, fostering a bidirectional empowerment model that underpins the emerging experiential economy. Ethical and human-centric frameworks address technological risks through interdisciplinary governance. Critical issues include ambiguous copyright attribution, as exemplified by landmark cases like \"The First AI Art Copyright Dispute,\" and algorithmic biases that perpetuate cultural misrepresentation, such as cultural appropriation or inappropriate expression. Academic efforts now prioritize synergistic governance that integrates technological, cultural, and legal perspectives to ensure that innovation aligns with humanistic principles. Collectively, these trajectories signify a paradigm shift from \"tool-enabled enhancement\" to \"ecosystem reconfiguration,\" establishing a robust framework for reevaluating artistic value in the age of intelligent technologies.\u003c/p\u003e \u003cp\u003eIn summary, the integration of artificial intelligence (AI) with visual art signifies both an inevitable trend of technological empowerment and a complex practice of cultural innovation. This study employs bibliometric analysis to systematically delineate research trends in AI-driven visual art, thereby offering data-driven insights for future scholarship. Additionally, the findings serve as actionable references for policymakers, artists, and technology developers aiming to enhance the synergy between AI and visual art. On a practical level, data-informed conclusions outline three pathways: (1) Policymakers should establish ethical guidelines for the commercialization of AI art and copyright attribution through algorithmic contribution weighting, ensuring equitable recognition for both human creators and algorithmic inputs; (2) Artists can leverage diffusion models and multimodal interaction to transcend traditional media constraints; (3) Technology developers must prioritize interpretability tools to reduce creative barriers while maintaining human-centric values. This closed-loop framework, encompassing both theory and practice, provides a comprehensive guide for collaboration between AI and visual art across cognitive and operational dimensions.\u003c/p\u003e \u003cp\u003eFuture research should advance breakthroughs in technological iteration, interdisciplinary integration, ethical governance, and methodological innovation. From a technological perspective, the development of interpretable generative models based on attention mechanisms could mitigate AI's encroachment on creative sovereignty. Additionally, integrating brain-computer interfaces and biosensing technologies may facilitate bidirectional real-time art systems that respond to emotional algorithms. Interdisciplinary efforts should prioritize the preservation of digital cultural heritage, exemplified by multispectral reconstruction and generative restoration of Dunhuang murals, as well as the exploration of neuroaesthetic mechanisms, such as the correlations between fMRI-quantified dopamine release and aesthetic preferences in response to AI-generated art. Ethically, there is an urgent need for a multimodal evaluation system that incorporates metrics of cultural diversity, including non-Western style representation, and social inclusivity, particularly regarding accessibility for disabled communities. Methodologically, hybrid approaches that combine topic modeling with artist oral histories can effectively bridge cognitive gaps between technologists and creators. Furthermore, multilingual database analyses, such as those conducted on Scopus and CNKI, can elucidate global acceptance patterns and the cultural-contextual modulation of technology adoption. Collectively, these proposed directions offer systematic, cross-disciplinary solutions for reconfiguring art ecosystems in the intelligent age.\u003c/p\u003e"},{"header":"7. Limitations","content":"\u003cp\u003eSeveral limitations still remain in this study. First, the analysis was conducted using CiteSpace and other software tools on raw data extracted exclusively from the Web of Science, with a primary focus on English-language publications. Due to technical constraints, we were unable to evaluate non-English literature or studies in other languages simultaneously. Furthermore, the employed software tools were unable to comprehensively collect all author-related data, reconcile name variations across formats for individual authors, or assess the methodological accuracy of the cited research in this field. The current analysis specifically examines research trends and focal points in artificial intelligence applications within the visual arts over the past 24 years. We intend to continue this longitudinal investigation beyond the publication of this report to track temporal developments. Additionally, our exclusive reliance on the Web of Science database has omitted potentially relevant publications from other significant repositories, including Google Scholar, ProQuest, SpringerLink, and CNKI. Future studies should broaden data sources to facilitate more comprehensive analyses and better capture the evolving dynamics of the field. Lastly, the predominant use of keyword co-occurrence analysis may have overlooked important but low-frequency research themes. For instance, the term \"artificial intelligence\" encompasses diverse research directions that our methodology might not fully distinguish. Subsequent investigations could enhance analytical rigor by incorporating topic model approaches to improve thematic comprehensiveness.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical Approval:\u003c/h2\u003e \u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed Consent:\u003c/strong\u003e \u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003e \u003cb\u003eAcknowledgments\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eConflict of interest:\u003c/strong\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZY: Formal analysis, investigation, visualization, Writing original draft. SY: Data curation, Investigation, Writing \u0026ndash; review \u0026amp; editing. JW: Supervision, Visualization, Writing \u0026ndash; review \u0026amp; editing. QW: Conceptualization, Methodology, Supervision.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJora O-D, Iacob M, Rosca VI et al (2024) Artificial intelligence and artistic imagination: Revisiting the cultural economy of industrial revolutions. Amfiteatru Economic 26(66):613\u0026ndash;632\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu R (2025) The Philosophical and Axiological Dimensions of AI in Art: Implications for Creativity and Cultural Value in Artistic Production and Marketing. 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Front Ecol Environ 17(2):109\u0026ndash;116\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePunj N, Ahmi A, Tanwar A et al (2023) Mapping the field of green manufacturing: A bibliometric review of the literature and research frontiers. J Clean Prod 423:138729\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLozano S, Calzada-Infante L, Adenso-D\u0026iacute;az B et al (2019) Complex network analysis of keywords co-occurrence in the recent efficiency analysis literature. Scientometrics 120:609\u0026ndash;629\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng F-F, Huang Y-W, Yu H-C et al (2018) Mapping knowledge structure by keyword co-occurrence and social network analysis: Evidence from Library Hi Tech between 2006 and 2017. Libr Hi Tech 36(4):636\u0026ndash;650\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKunisch S, Denyer D, Bartunek JM et al (2023) Review research as scientific inquiry. 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Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Artificial intelligence, Visual arts, Bibliometrics, Citespace, VOSviewer","lastPublishedDoi":"10.21203/rs.3.rs-7766646/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7766646/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid integration of artificial intelligence (AI) and visual arts has driven profound paradigm shifts in art creation, market dynamics, and cultural exchange. This bibliometric analysis of 379 publications from the Web of Science Core Collection (2001\u0026ndash;2024) describes the current applications of AI in visual arts and future challenges. We cre-ated a visual collaborative network mapping connecting AI and visual arts using biblio-metric methods, which included knowledge foundations, research trends, nation-al/institutional contributions, and emerging frontiers. While clustering analysis identifies current research objectives focused on Generative Adversarial Networks (GANs), co-citation analysis demonstrates theoretical progress within the field. Important conclusions show that China leads the world in academic output and influence, while the US has the highest level of international collaboration. Humanities, Arts, and Computer Science are among the core areas, with Applied Sciences-Basel (ISSN 2076\u0026ndash;3417) emerging as the most prolific journal. While developing paths prioritize eco-conscious generative models, neurocognitive mechanisms of human-AI co-creation, and environment-responsive AI art design, current research focuses on algorithmic advancements in AI-generated art, ethical judgments, and changes in art markets. This study establishes a framework for comprehending the evolution in AI-driven visual arts and provides empirical insights to inform technological practices and multidisciplinary synergies.\u003c/p\u003e","manuscriptTitle":"Innovations in Visual Arts Driven by Artificial Intelligence: Global Research Hotspots and Emerging Trends","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 08:35:58","doi":"10.21203/rs.3.rs-7766646/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-04T21:47:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205479893041041771064802530696607637239","date":"2026-02-22T11:03:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258463214445267200999755864031404384656","date":"2026-02-20T02:08:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-19T23:27:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46616551219216954981266446299832731577","date":"2026-02-14T22:39:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-09T09:06:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-04T08:38:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-24T15:14:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-05T11:51:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-11-05T10:45:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"08916e62-099d-4b66-b272-d19f022e1bf2","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62594803,"name":"Humanities/Complex networks"},{"id":62594804,"name":"Social science/Complex networks"},{"id":62594805,"name":"Physical sciences/Mathematics and computing"},{"id":62594806,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-02-11T08:35:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 08:35:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7766646","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7766646","identity":"rs-7766646","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}