DentVLM: A Multimodal Vision-Language Model for Comprehensive Dental Diagnosis and Enhanced Clinical Practice

• URL: http://arxiv.org/abs/2509.23344v1
• Date: Sat, 27 Sep 2025 14:47:37 GMT
• Title: DentVLM: A Multimodal Vision-Language Model for Comprehensive Dental Diagnosis and Enhanced Clinical Practice
• Authors: 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, Lunguo Xia, Bing Fang, Jimeng Sun, Jian Wu, Zuozhu Liu,
• Abstract summary: We introduce DentVLM, a vision-language model engineered for expert-level oral disease diagnosis.<n>The model is capable of interpreting seven 2D oral imaging modalities across 36 diagnostic tasks.<n>It surpassed the diagnostic performance of 13 junior dentists on 21 of 36 tasks and exceeded that of 12 senior dentists on 12 of 36 tasks.
• Score: 71.62725911420627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diagnosing and managing oral diseases necessitate advanced visual interpretation across diverse imaging modalities and integrated information synthesis. While current AI models excel at isolated tasks, they often fall short in addressing the complex, multimodal requirements of comprehensive clinical dental practice. Here we introduce DentVLM, a multimodal vision-language model engineered for expert-level oral disease diagnosis. DentVLM was developed using a comprehensive, large-scale, bilingual dataset of 110,447 images and 2.46 million visual question-answering (VQA) pairs. The model is capable of interpreting seven 2D oral imaging modalities across 36 diagnostic tasks, significantly outperforming leading proprietary and open-source models by 19.6% higher accuracy for oral diseases and 27.9% for malocclusions. In a clinical study involving 25 dentists, evaluating 1,946 patients and encompassing 3,105 QA pairs, DentVLM surpassed the diagnostic performance of 13 junior dentists on 21 of 36 tasks and exceeded that of 12 senior dentists on 12 of 36 tasks. When integrated into a collaborative workflow, DentVLM elevated junior dentists' performance to senior levels and reduced diagnostic time for all practitioners by 15-22%. Furthermore, DentVLM exhibited promising performance across three practical utility scenarios, including home-based dental health management, hospital-based intelligent diagnosis and multi-agent collaborative interaction. These findings establish DentVLM as a robust clinical decision support tool, poised to enhance primary dental care, mitigate provider-patient imbalances, and democratize access to specialized medical expertise within the field of dentistry.

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