Key result
Valvular heart disease AI research expands across three phases, reaching ~3,300 publications focused on deep learning.
Why the study?
Although artificial intelligence applications in valvular heart disease have advanced significantly, a systematic evaluation of the historical development, knowledge structure, and global research patterns in this field had not yet been conducted.
This bibliometric analysis demonstrates that artificial intelligence research in valvular heart disease has evolved over 35 years through three distinct phases—from assistive imaging quantification to predictive modeling, and currently to deep learning and multimodal clinical integration.
Answers are grounded in this paper and cite their sources.
AI research output in valvular heart disease surged across three phases; leaves open prospective validation before deep learning integration affects clinical practice.
AIMS/BACKGROUND: The application of artificial intelligence (AI) in valvular heart disease (VHD) has advanced significantly in recent years. However, a systematic evaluation of this field's historical development, knowledge structure, and global research patterns has yet to be conducted. This paper aims to outline the global evolution of AI research in VHD utilizing bibliometric and knowledge-mapping methods. METHODS: Publications related to AI and VHD from 1991 to 2025 were retrieved from the Web of Science Core Collection database to investigate research progress. CiteSpace software was employed for bibliometric and knowledge mapping analyses, assessing indicators such as publication trends, keyword networks, collaboration patterns, co-citation relationships, and emerging topics. RESULTS: A total of 3291 pieces of literature were identified. Research productivity remained low until the year before 2004, followed by steady growth and a sharp increase after 2015, culminating in 461 articles by the end of 2025. Network analysis revealed three sequential stages: image-assisted applications, prediction modeling, and recent advancements in deep-learning-based integrated systems. The United States accounted for the largest research volume, while England and Germany served as core nodes in the international cooperation network. Co-citation analysis identified significant clinical guidelines and pioneering interventional studies as key intellectual pillars in this domain. CONCLUSION: Over the past 35 years, significant progress has been achieved, transitioning from computer systems used solely for image analysis to more integrated clinical applications. Currently, deep learning techniques are increasingly prevalent, accompanied by a focus on multimodal data integration and a stronger linkage between computational methods and clinical decision-making.
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Wang et al. (2026) studied Valvular heart disease (n=3,291). Artificial intelligence applications in valvular heart disease was evaluated on Bibliometric publication output and keyword network modularity (Q) and silhouette (S) scores across three evolutionary phases. Artificial intelligence research in valvular heart disease grew to 3,291 publications (peaking at 461 in 2025) across 3 phases (Q=0.8165, S=0.9337) toward deep learning integration.
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