Towards Generalist Intelligence in Dentistry: Vision Foundation Models for Oral and Maxillofacial Radiology

• URL: http://arxiv.org/abs/2510.14532v1
• Date: Thu, 16 Oct 2025 10:24:23 GMT
• Title: Towards Generalist Intelligence in Dentistry: Vision Foundation Models for Oral and Maxillofacial Radiology
• Authors: Xinrui Huang, Fan Xiao, Dongming He, Anqi Gao, Dandan Li, Xiaofan Zhang, Shaoting Zhang, Xudong Wang,
• Abstract summary: DentVFM is the first family of vision foundation models (VFMs) designed for dentistry.<n>It generates task-agnostic visual representations for a wide range of dental applications.<n>It shows impressive generalist intelligence, demonstrating robust generalization to diverse dental tasks.
• Score: 22.124686092997717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Oral and maxillofacial radiology plays a vital role in dental healthcare, but radiographic image interpretation is limited by a shortage of trained professionals. While AI approaches have shown promise, existing dental AI systems are restricted by their single-modality focus, task-specific design, and reliance on costly labeled data, hindering their generalization across diverse clinical scenarios. To address these challenges, we introduce DentVFM, the first family of vision foundation models (VFMs) designed for dentistry. DentVFM generates task-agnostic visual representations for a wide range of dental applications and uses self-supervised learning on DentVista, a large curated dental imaging dataset with approximately 1.6 million multi-modal radiographic images from various medical centers. DentVFM includes 2D and 3D variants based on the Vision Transformer (ViT) architecture. To address gaps in dental intelligence assessment and benchmarks, we introduce DentBench, a comprehensive benchmark covering eight dental subspecialties, more diseases, imaging modalities, and a wide geographical distribution. DentVFM shows impressive generalist intelligence, demonstrating robust generalization to diverse dental tasks, such as disease diagnosis, treatment analysis, biomarker identification, and anatomical landmark detection and segmentation. Experimental results indicate DentVFM significantly outperforms supervised, self-supervised, and weakly supervised baselines, offering superior generalization, label efficiency, and scalability. Additionally, DentVFM enables cross-modality diagnostics, providing more reliable results than experienced dentists in situations where conventional imaging is unavailable. DentVFM sets a new paradigm for dental AI, offering a scalable, adaptable, and label-efficient model to improve intelligent dental healthcare and address critical gaps in global oral healthcare.

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