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<p>Oral diseases affect billions of people, yet specialist dental expertise remains unevenly distributed, and diagnosis often requires synthesis across diverse imaging modalities. Existing artificial intelligence systems mostly address isolated tasks, limiting their applicability in comprehensive dental assessment. Here we introduce DentVLM, a dental vision-language model that jointly interprets images and text, supports expert-level oral disease diagnosis across seven dental imaging modalities and 36 tasks. Developed using 110,447 images and 2.46 million bilingual visual question-answer pairs, DentVLM outperforms leading proprietary, open-source and domain-specific medical models on internal and external tests. In a study of 32 participants, DentVLM surpasses junior readers, matches intermediate general practitioners and approaches senior specialists. In collaborative workflows, it raises junior and intermediate readers toward specialist-level performance and reduces diagnostic time for all readers by 15.0-37.0%. These results establish DentVLM as a clinical decision support tool for reducing specialist care gaps and broadening access to high-quality dental expertise.</p>

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A multimodal vision-language model for comprehensive dental diagnosis and enhanced clinical practice

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月22日

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  • Zijie Meng,
  • Jin Hao,
  • Xiwei Dai,
  • Yang Feng,
  • Jiaxiang Liu,
  • Bin Feng,
  • Huikai Wu,
  • Xiaotang Gai,
  • Hengchuan Zhu,
  • Tianxiang Hu,
  • Yangyang Wu,
  • Hongxia Xu,
  • Jin Li,
  • Jun Xiao,
  • Xiaoqiang Liu,
  • Joey Tianyi Zhou,
  • Fudong Zhu,
  • Zhihe Zhao,
  • Bing Fang,
  • Lunguo Xia,
  • Jimeng Sun,
  • Jian Wu,
  • Zuozhu Liu

摘要

Oral diseases affect billions of people, yet specialist dental expertise remains unevenly distributed, and diagnosis often requires synthesis across diverse imaging modalities. Existing artificial intelligence systems mostly address isolated tasks, limiting their applicability in comprehensive dental assessment. Here we introduce DentVLM, a dental vision-language model that jointly interprets images and text, supports expert-level oral disease diagnosis across seven dental imaging modalities and 36 tasks. Developed using 110,447 images and 2.46 million bilingual visual question-answer pairs, DentVLM outperforms leading proprietary, open-source and domain-specific medical models on internal and external tests. In a study of 32 participants, DentVLM surpasses junior readers, matches intermediate general practitioners and approaches senior specialists. In collaborative workflows, it raises junior and intermediate readers toward specialist-level performance and reduces diagnostic time for all readers by 15.0-37.0%. These results establish DentVLM as a clinical decision support tool for reducing specialist care gaps and broadening access to high-quality dental expertise.