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<p><b>Objectives</b> This study employs a bibliometric analysis to explore the forefront and research directions of artificial intelligence (AI) in the field of dentistry, aiming to understand the current status and limitations of AI applications in dentistry and provide insights for the future development of AI applications in dentistry.</p><p><b>Methods</b> Literature data were retrieved from the Web of Science Core Collections database. CiteSpace was used for authorship, country, institution, and keyword analysis, with results visualised accordingly.</p><p><b>Results</b> Research on AI applications in dentistry has surged since 2019 and shows an increasing trend annually. China and the United States have the most co-occurring publications in this field. Keyword clustering analysis reveals two main research directions: technological applications, and dental subspecialty research. Keyword burst analysis highlights prolonged attention to convolutional neural networks and supervised machine learning.</p><p><b>Conclusion</b> Bibliometric analysis indicates that current research hotspots in the field primarily focus on oral disease diagnosis and treatment methods. However, further development and optimisation in AI models, as well as its training data and its capabilities for comprehensive analysis, are needed to realise its full potential in this field.</p>

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Artificial intelligence in dentistry: a bibliometric analysis

  • 期刊论文

  • 发表日期：

    2025年10月17日

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  • Jiangheng Long,
  • Lei Yang,
  • Jinge Dou,
  • Lan Zhang,
  • Xuelian Tan

摘要

Objectives This study employs a bibliometric analysis to explore the forefront and research directions of artificial intelligence (AI) in the field of dentistry, aiming to understand the current status and limitations of AI applications in dentistry and provide insights for the future development of AI applications in dentistry.

Methods Literature data were retrieved from the Web of Science Core Collections database. CiteSpace was used for authorship, country, institution, and keyword analysis, with results visualised accordingly.

Results Research on AI applications in dentistry has surged since 2019 and shows an increasing trend annually. China and the United States have the most co-occurring publications in this field. Keyword clustering analysis reveals two main research directions: technological applications, and dental subspecialty research. Keyword burst analysis highlights prolonged attention to convolutional neural networks and supervised machine learning.

Conclusion Bibliometric analysis indicates that current research hotspots in the field primarily focus on oral disease diagnosis and treatment methods. However, further development and optimisation in AI models, as well as its training data and its capabilities for comprehensive analysis, are needed to realise its full potential in this field.