Trends and hotspots in artificial intelligence applications for atherosclerosis research: A bibliometric analysis

Abstract

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.