Global research landscape on artificial intelligence in echocardiography from 1997 to 2024: Bibliometric analysis
Objective With the widespread use of artificial intelligence (AI) in medical imaging, research on AI-powered echocardiography has gained increasing attention. However, a systematic study of global research trends and key developments in this area remains limited. The study aims to explore the curren...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-06-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251351201 |
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| Summary: | Objective With the widespread use of artificial intelligence (AI) in medical imaging, research on AI-powered echocardiography has gained increasing attention. However, a systematic study of global research trends and key developments in this area remains limited. The study aims to explore the current research hotspots of AI-driven echocardiography by bibliometric methods, providing data support and academic insights for future research. Methods The Web of Science Core Collection database was utilized to search articles in this area from 1997 to 2024. The filtered data were analyzed and visualized by VOSviewer and CiteSpace software. Results In total, 605 documents were involved, since 2020, there has been an exponential increase in the publications. The United States held the top position in both the volume of publications and citation counts. And the top organization in citations was Stanford University. Three authors with the most publications were Lovstakken Lasse, Ouyang David, and Sengupta Partho P. The journal with the most citations was the Journal of the American Society of Echocardiography . Based on keyword analysis, the current research hotspots were mainly focused on image segmentation, heart failure, deep learning, and pulmonary hypertension. Conclusion Research on the application of AI in echocardiography is currently flourishing, with broad prospects. In the future, it is crucial to promote interdisciplinary collaboration on an international scale, especially between countries and research institutions. Future research will focus on developing large language models that can integrate multimodal information, while also addressing key issues such as improving model interpretability. |
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| ISSN: | 2055-2076 |