首个大规模口腔多模态数据集，助力AI精准诊断牙科问题。
A benchmark multimodal oro-dental dataset for large vision-language models
- 收集8年临床数据，含5万张口内图、8056张牙片及病历文本
- 微调Qwen-VL模型在六类牙科异常分类上显著优于基线模型
- 适合医疗AI研究者、牙科数字化团队使用，推动智能诊疗发展
人工智能在口腔健康领域的进步依赖于大规模多模态数据集。本文构建了一个综合性多模态数据集，涵盖2018至2025年间4800名患者（年龄10至90岁）的8775次牙科检查，包含50000张口内影像、8056张牙片及详细的文本记录，如诊断、治疗计划与随访笔记。数据遵循标准伦理规范采集并标注用于基准测试。为验证其价值，我们微调了前沿大视觉语言模型Qwen-VL 3B和7B，在两类任务上评估：六类口腔异常分类与基于多模态输入生成完整诊断报告。结果表明，微调模型显著优于原始模型及GPT-4o，证实该数据集对推动AI驱动口腔医疗解决方案的有效性。数据集已公开，为未来牙科AI研究提供关键资源。
原文摘要 · Abstract (English)
The advancement of artificial intelligence in oral healthcare relies on the availability of large-scale multimodal datasets that capture the complexity of clinical practice. In this paper, we present a comprehensive multimodal dataset, comprising 8775 dental checkups from 4800 patients collected over eight years (2018-2025), with patients ranging from 10 to 90 years of age. The dataset includes 50000 intraoral images, 8056 radiographs, and detailed textual records, including diagnoses, treatment plans, and follow-up notes. The data were collected under standard ethical guidelines and annotated for benchmarking. To demonstrate its utility, we fine-tuned state-of-the-art large vision-language models, Qwen-VL 3B and 7B, and evaluated them on two tasks: classification of six oro-dental anomalies and generation of complete diagnostic reports from multimodal inputs. We compared the fine-tuned models with their base counterparts and GPT-4o. The fine-tuned models achieved substantial gains over these baselines, validating the dataset and underscoring its effectiveness in advancing AI-driven oro-dental healthcare solutions. The dataset is publicly available, providing an essential resource for future research in AI dentistry.
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