DentVLM可精准诊断36种口腔疾病，助力医生高效诊疗。
DentVLM: A Multimodal Vision-Language Model for Comprehensive Dental Diagnosis and Enhanced Clinical Practice
- 基于11万张图像和246万条问答对，融合7种口腔影像模态进行多任务诊断。
- 在36项任务中准确率比主流模型高19.6%，对错颌诊断提升27.9%。
- 实测可让新手医生水平达资深医师，且缩短15%-22%诊断时间。
口腔疾病诊断与管理需跨多种成像模态的高级视觉解析与信息整合。现有AI模型虽在单一任务上表现优异，却难以满足复杂临床需求。本文提出DentVLM，一种面向专家级口腔疾病诊断的多模态视觉语言模型。该模型基于包含110,447张图像和246万条视觉问答（VQA）对的大型双语数据集训练而成，可处理7种二维口腔影像模态下的36项诊断任务。其在口腔疾病诊断上的准确率较领先开源与专有模型高出19.6%，错颌诊断准确率提升27.9%。临床测试中，25名牙医评估1,946名患者，共生成3,105组问答，DentVLM在36项任务中超越13名初级牙医中的21项，超过12名高级牙医中的12项。在协同工作流程中，使初级牙医表现达到高级水平，并将所有从业者诊断时间缩短15%-22%。此外，该模型在家庭口腔健康管理、医院智能诊断及多智能体协作等实际场景中也展现出良好应用前景。结果表明，DentVLM是强有力的临床决策支持工具，有望提升基层牙科医疗质量，缓解医护资源不均，推动专科医疗知识普及。
原文摘要 · Abstract (English)
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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