A comprehensive bibliometric analysis of 594 peer-reviewed publications indexed in Scopus between 2012 and April 2024 reveals a marked acceleration in research output after 2018, with the United States, China, and the United Kingdom emerging as dominant contributors and central hubs in international collaboration networks.
Abstract
Artificial intelligence (AI) has increasingly become a central component of healthcare research, yet a systematic understanding of its scholarly evolution remains limited. This study presents a comprehensive bibliometric analysis of 594 peer-reviewed publications indexed in Scopus between 2012 and April 2024, retrieved using a focused title-based search strategy. Using VOSviewer, we examine publication and citation trends, leading contributing countries and institutions, and thematic structures through keyword co-occurrence networks. Results reveal a marked acceleration in research output after 2018, with the United States, China, and the United Kingdom emerging as dominant contributors and central hubs in international collaboration networks. Keyword analysis indicates a strong methodological emphasis on machine learning, deep learning, and medical imaging, while comparatively limited attention is given to ethical, implementation, and equity-related themes. These findings highlight both the rapid growth and the thematic concentration of AI-in-healthcare research, underscoring the need for future studies to address translational and governance challenges alongside technical innovation.
The aim of this study is to analyze trends in the literature regarding the economic aspects of artificial intelligence use in the healthcare sector through bibliometric mapping of scientific publications and to present a comprehensive overview of the field’s current structure based on quantitative indicators. In this study, 1,326 studies obtained from a search conducted on February 15, 2026, in the Web of Science database using specified keywords were examined within the scope of bibliometric analysis using the VOSviewer program. The findings indicate that publications peaked in 2025, that the United States is the most productive country, and that artificial intelligence, machine learning, and deep learning are the most prominent research topics. Additionally, Olga Golubnitschaja, Wang Wei, and Carl Erb were identified as leading researchers in the field. Furthermore, the field was found to have a strong interdisciplinary structure, with research output being predominantly technology-focused and concentrated in high-impact-factor journals such as IEEE Access, Sensors, and Nature Communications; the most influential institutions were Stanford University, the National University of Singapore, and Harvard Medical School; and the most cited countries were the United States, India, and China. Furthermore, the United States, India, and China have stood out in terms of citation performance. Consequently, it is assessed that research on the economic impacts of AI in healthcare will continue to be important in the future.
Osman Şahman, Semih Islıcık· Sağlık ve Sosyal Refah Araşt...· 0 citations
Chronic diseases represent one of the most critical fields in healthcare systems, driving the majority of global deaths and healthcare costs. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have increasingly shown their potential in disease diagnosis, prediction and management. In this study, we conducted a bibliometric analysis of the scientific literature on AI and ML applications in chronic disease. We applied a structured multi-criteria selection process in different steps to retain 566 publications for analysis. Publication trends, geographic distribution, journals, and keyword cooccurrence patterns were examined using multiple tools to analyse the retrieved documents. Results show an acceleration in research growth and the application of numerous AI approaches in various fields of chronic disease. However, we found a concentration of study and activity around some diseases and countries. These findings provide a consolidated overview of current research dynamics and establish a foundation for future investigations and foster more balanced international collaboration in chronic disease management.
Zakaria Slimani, Hanae Al kaddouri, A. Azizi et al.· EPJ Web of Conferences· 0 citations
Background Atherosclerosis (AS) is a complex systemic, immune-inflammatory vascular disease, and there is an urgent need to innovate its diagnostic and therapeutic strategies. The rapid advancement of artificial intelligence (AI) technology has opened up new avenues for the early diagnosis, risk prediction, and precision treatment of AS. However, a systematic quantitative analysis of global knowledge and collaboration in this field is still lacking. Methods This study conducted a systematic search of the Web of Science Core Collection (WoSCC), Scopus, and PubMed databases for literature on the application of AI in AS research published between 2000 and 2025. Advanced bibliometric tools, such as CiteSpace, VOSviewer, and R-Bibliometrix, were employed to conduct multidimensional visual analyses of publication trends, transnational collaboration networks, knowledge flows in core journals, and the emergence of keywords. Results A total of 258 core publications were ultimately included, involving 1,900 authors, 44 countries/regions, and 1,152 research institutions. The spatio-temporal distribution revealed that the volume of publications in this field has grown significantly, with rapid growth beginning after 2021. Globally, a China-U.S. dual-center pattern has emerged: China leads in terms of output scale and growth rate, while the United States holds higher citation accumulation in terms of total citations and average citations per paper. However, raw citation counts and average citations per paper are influenced by publication year, document type, and denominator size; these cross-country differences should therefore be interpreted with caution, and future studies using normalized indicators are warranted. The evolution of research hotspots has undergone a distinct three-stage transition: from early basic algorithm development (2000–2014), through multimodal image intelligent analysis (2015–2020), to the current phase of clinical multicenter validation and in-depth mechanism decoding (2021–2025). Journal overlay analysis further confirms that clinical medicine is increasingly intersecting with basic life sciences, driving bidirectional knowledge transfer. Conclusions This study systematically maps the global research landscape of AI in the field of AS, revealing its evolutionary path from methodological exploration to clinical translation. Future research should break down data barriers, advance multinational, multiethnic, and multicenter cohort validation, and focus on the development of interpretable AI models. The observed trends in the bibliometric data suggest that the integration of AI technology with systems biology and a holistic medical approach may play an increasingly important role in personalized, precision interventions for AS.
Ye Lv, Si-Yuan Sun, Yu-Zhuo Zhang et al.· Digital Health· 0 citations
This study aimed to analyze trends, knowledge structure, and academic collaboration networks in the field of Artificial Intelligence (AI) and digital technology in mental health using bibliometric analysis. Data were retrieved from the Dimensions database for the period 2017–2025 using keywords related to AI, digital technology, and mental health. Document selection followed the PRISMA guidelines, yielding 145 eligible articles, which were subsequently analyzed using VOSviewer and Biblioshiny. The findings revealed an exponential growth pattern in publication output, with a notable surge from 7 articles in 2021 to 61 articles in 2025, reflecting heightened global research interest in this domain. In terms of country-level distribution, China led with 40 documents and 474 citations, while the United States and the United Kingdom demonstrated higher per-document citation impact, highlighting the distinction between research quantity and quality. Journal co-citation network analysis identified four prominent journal clusters spanning digital psychology and behavioral science, clinical medicine, psychiatry and mental health, and developmental and educational sciences. Keyword co-occurrence analysis further delineated five major research clusters encompassing clinical and social dimensions, bibliometric methodology, public health, and the application of technology in international contexts. These findings indicate that this research field is highly interdisciplinary and remains in an expansive growth phase. The results offer valuable insights for researchers, academics, and policymakers in strategically directing future research on digital mental health.
BackgroundArtificial intelligence (AI) is transforming healthcare by influencing clinical decision-making, service delivery, and professional roles. Healthcare professionals' perceptions of AI are critical to its successful implementation and integration into clinical practice. Understanding research trends in this field can support future scientific and policy developments.ObjectiveThis study aimed to examine the scientific structure, research trends, and thematic development of the literature on healthcare professionals' perceptions of artificial intelligence through bibliometric analysis.MethodsA bibliometric analysis was conducted using publications indexed in the Scopus database between 2015 and 2025. The dataset included English-language, peer-reviewed journal articles in medicine, nursing, and health sciences. A total of 644 publications met the inclusion criteria. Publication trends, productive countries, authors, subject areas, and citation patterns were analyzed. Network visualization and keyword co-occurrence analyses were performed using VOSviewer.ResultsPublications increased markedly after 2020, indicating growing scientific interest in the topic. Three major thematic clusters were identified: clinical and technological integration of AI in healthcare, AI in health education and professional practice, and ethical, psychological, and attitudinal aspects of AI. The findings also revealed a shift toward human-centered research emphasizing healthcare professionals' competencies, acceptance, and concerns regarding AI.ConclusionsThis study provides a comprehensive overview of the intellectual landscape of research on healthcare professionals' perceptions of AI. The findings may inform future research, evidence-based policymaking, and strategies to support the effective integration of AI into healthcare systems.
This study examines the global research trends and development of Health Research Information Systems (HRIS) through bibliometric analysis. Publications indexed in the Scopus database from 1972 to 2026 were taken for the study research. HRIS has become an essential tool for supporting evidence-based healthcare decision-making, knowledge generation, and policy formulation. The study aims to identify major research trends, influential contributors, collaboration patterns, and emerging thematic areas within the field. Bibliometric data were analyzed using VOSviewer and Bibliometrix (R-package) to evaluate publication growth, leading authors, institutions, countries, citation impact, and keyword co-occurrence networks. The findings reveal a steady increase in HRIS-related research over the years, reflecting its growing importance in global healthcare systems. China emerged as the leading contributor in terms of publications and citations, followed by the United States, Australia, Spain, and the United Kingdom. The analysis also shows that most researchers contributed only a single publication, indicating a diverse and multidisciplinary research landscape. The study highlights the evolving intellectual structure of HRIS and emphasizes the need for integrating advanced technologies, improving data-sharing standards, and strengthening collaboration between developed and developing regions. Future advancements in artificial intelligence, machine learning, and open data ecosystems may further transform HRIS into a robust platform for global health research and policy innovation.
Ajit Kumar Pradhan, M. Mishra, Susama Nanda· IP Indian Journal of Library...· 0 citations
