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

2025-09-27

AI Summary

Current AI models struggle to integrate multimodal oral imaging data and support complex clinical dental diagnosis. Method: We introduce the first large-scale bilingual multimodal dental dataset—comprising 110,000 images and 2.46 million vision-language question-answer pairs—covering seven types of 2D imaging modalities and 36 diagnostic tasks. We further propose a novel vision-language model architecture specifically designed for comprehensive dental diagnosis, featuring deep cross-modal understanding and clinical knowledge reasoning. Contribution/Results: In clinical validation involving 25 dentists, our model outperformed junior clinicians on 21 tasks and senior clinicians on 12 tasks, while reducing average diagnostic time by 15–22%. The framework effectively bridges the expertise gap in dental care, enabling multi-institutional collaboration, remote home-based health monitoring, and intelligent hospital-assisted diagnosis.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Developing multimodal AI for comprehensive dental diagnosis
• Addressing limitations of current AI in clinical dental practice
• Enhancing diagnostic accuracy and efficiency across oral diseases

Innovation

• Multimodal vision-language model for expert dental diagnosis
• Uses large bilingual dataset with visual question-answering pairs
• Interprets seven oral imaging modalities across diagnostic tasks