MetaDent: Labeling Clinical Images for Vision-Language Models in Dentistry

2026-04-16

AI Summary

This work addresses the lack of fine-grained annotations and comprehensive evaluation benchmarks for intraoral dental photography, which hinders the application of vision-language models (VLMs) in clinical dentistry. The authors propose a meta-annotation framework tailored to clinical dental images, constructing a large-scale dataset of 60,669 images, with 2,588 images annotated using a semi-structured scheme that combines image-level summaries and free-text descriptions of abnormal regions. Leveraging large language models, they generate approximately 15,000 standardized visual question-answer pairs and establish an 18-category multi-label classification benchmark, both rigorously validated by human experts alongside error analysis. This study presents the first comprehensive evaluation suite for dental VLMs, demonstrating through experiments that state-of-the-art models still exhibit significant limitations in fine-grained visual understanding tasks.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Vision-Language Models (VLMs) have demonstrated significant potential in medical image analysis, yet their application in intraoral photography remains largely underexplored due to the lack of fine-grained, annotated datasets and comprehensive benchmarks. To address this, we present MetaDent, a comprehensive resource that includes (1) a novel and large-scale dentistry image dataset collected from clinical, public, and web sources; (2) a semi-structured annotation framework designed to capture the hierarchical and clinically nuanced nature of dental photography; and (3) comprehensive benchmark suites for evaluating state-of-the-art VLMs on clinical image understanding. Our labeling approach combines a high-level image summary with point-by-point, free-text descriptions of abnormalities. This method enables rich, scalable, and task-agnostic representations. We curated 60,669 dental images from diverse sources and annotated a representative subset of 2,588 images using this meta-labeling scheme. Leveraging Large Language Models (LLMs), we derive standardized benchmarks: approximately 15K Visual Question Answering (VQA) pairs and an 18-class multi-label classification dataset, which we validated with human review and error analysis to justify that the LLM-driven transition reliably preserves fidelity and semantic accuracy. We then evaluate state-of-the-art VLMs across VQA, classification, and image captioning tasks. Quantitative results reveal that even the most advanced models struggle with a fine-grained understanding of intraoral scenes, achieving moderate accuracy and producing inconsistent or incomplete descriptions in image captioning. We publicly release our dataset, annotations, and tools to foster reproducible research and accelerate the development of vision-language systems for dental applications.

Problem

• Vision-Language Models
• Intraoral Photography
• Fine-grained Annotation
• Dental Image Analysis
• Benchmarking

Innovation

• Vision-Language Models
• Meta-labeling
• Dental Image Annotation
• Visual Question Answering
• Large Language Models