Visualization of artificial intelligence applications in oral disease diagnosis: A bibliometric analysis

This study aims to perform a comprehensive visualization-based analysis of the research status, thematic hotspots, and developmental trends in AI-assisted oral disease diagnosis over the past two decades, thereby offering valuable references for future research in this fields.

We conducted a bibliometric study with 2,131 documents extracted from the Web of Science Core Collection (2005-2025) using CiteSpace to systematically analyze publication trends, major countries, institutions, journals and co-citation patterns. Visualizations including collaboration networks, keyword co-occurrence clusters, citation bursts, and topic timelines showed the evolving intellectual structure and emerging research fronts in this area.

The number of annual publications grew exponentially and peaked at 519 in 2024. China, the United States and India ranked as the top three countries. Berlin-based institutions contributed 224 publications, representing 45.62% of the 491 outputs from the top ten productive institutions. Core keywords were identified through co-occurrence analysis, including "artificial intelligence", "deep learning", "machine learning", and "classification". Further cluster analysis formed 15 clusters, which were summarized into three major themes: clinical diseases, technical approaches, and cross-cutting integration. Burst analysis showed that "Computer-aided diagnosis" had the strongest burst (5.23), followed by "system" (4.75) and "extractions" (4.69).

In this study, we used bibliometric visualization analysis to explore the evolution process and main research areas of AI-aided diagnosis for oral diseases between 2005 and 2025, identified new research areas, and provided useful guidance on future research and application topics.