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

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. DentVLM is a dental vision-language model developed to support dental diagnosis across seven oral imaging modalities and 36 tasks. It matches intermediate general practitioners, approaches senior specialists, and reduces diagnostic time by 15.0-37.0% in collaborative clinical workflows.

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Publication Details

Journal
Nature Communications
Published
2026-07-22
DOI
https://doi.org/10.1038/s41467-026-75718-x
Primary Topic
Multimodal Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

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

J Liu, Zuozhu Liu, Joey Tianyi Zhou, Jin Hao et al.
Nature Communications
Multimodal Machine Learning Applications
article

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

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

Abstract

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. DentVLM is a dental vision-language model developed to support dental diagnosis across seven oral imaging modalities and 36 tasks. It matches intermediate general practitioners, approaches senior specialists, and reduces diagnostic time by 15.0-37.0% in collaborative clinical workflows.

Nature Communications
Agency for Science, Technology and Research (SG), University of Illinois Urbana-Champaign (US), Peking University (CN), Sichuan University (CN), Shanghai Ninth People's Hospital (CN), Zhejiang University Medical College Affiliated Stomatological Hospital (CN), Stomatology Hospital (CN), Shenzhen Second People's Hospital (CN), Artificial Intelligence in Medicine (Canada) (CA), State Key Laboratory of Oral Diseases, Zhejiang University of Technology (CN), Singapore Management University (SG)
Quality Education
Openalex Percentile: Top 8%
Multimodal Machine Learning Applications
DOIOpen Access PDFOpenAlex
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