Global research trends of artificial intelligence in echocardiography (1984–2025): a bibliometric analysis

Abstract Background Echocardiography has evolved substantially since its introduction in 1954. Artificial intelligence (AI) offers transformative potential for image acquisition, quantification, interpretation, and reporting. Despite growing research interest, a comprehensive evaluation of the historical trends and evolution of AI in echocardiography is lacking. The study aimed to conduct a comprehensive bibliometric analysis of the global research landscape in this field. Methods A systematic literature search was conducted using the Scopus and Web of Science Core Collection databases for publications on AI in echocardiography from inception through November 10, 2025; no language filter was applied. Data analysis was performed using Microsoft Excel, Microsoft Access, and IBM SPSS Statistics. In addition, bibliometric mapping and data visualization were conducted. Results Overall, 2682 publications received 35,083 citations (mean, 13.08 citations per publication), spanning 1984–2025. Original articles, conference papers, and review articles accounted for 87.4% and 98.5% of total publications and citations, respectively. Publication output remained limited until approximately 2015, rose gradually thereafter, and accelerated markedly after 2018, with similar output in 2024 and 2025. The United States led in both publication volume and total citations, followed by China and the United Kingdom. The United States also had the highest collaboration link strength, and seven of the top 10 institutions were in North America (six in the United States and one in Canada). Mayo Clinic ranked first, with 142 publications and 3604 citations, and had the earliest first indexed publication among the top institutions (1992). The journal JACC: Cardiovascular Imaging ranked first in both total citations (1,911) and citations per publication (36.75). “Echocardiography,” “deep learning,” “artificial intelligence,” and “machine learning” were the most frequently used terms. The thematic evolution showed continuity of algorithmic-centric themes (1984–2019) with emerging clinically oriented themes (2020–2025). Conclusions This comprehensive bibliometric analysis highlights the rapid expansion of AI research in echocardiography, characterized by an accelerating publication output and emerging clinically oriented research topics. These findings reflect growing research interest in integrating AI into echocardiographic practice and highlight the need for future research prioritizing multicenter validation, clinical translation, and equitable real-world implementation.