A benchmark multimodal oro-dental dataset for large vision-language models

• URL: http://arxiv.org/abs/2511.04948v1
• Date: Fri, 07 Nov 2025 03:14:20 GMT
• Title: A benchmark multimodal oro-dental dataset for large vision-language models
• Authors: Haoxin Lv, Ijazul Haq, Jin Du, Jiaxin Ma, Binnian Zhu, Xiaobing Dang, Chaoan Liang, Ruxu Du, Yingjie Zhang, Muhammad Saqib,
• Abstract summary: The dataset includes 50000 intraoral images, 8056 radiographs, and detailed textual records, including diagnoses, treatment plans, and follow-up notes.<n>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.<n>The dataset is publicly available, providing an essential resource for future research in AI dentistry.
• Score: 5.063576567382722
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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