mlsanityData to 14 Oct 2026

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

arXiv 2511.04948 ↗First posted 7 Nov 2025cs.CV cs.AIcited by 0

On the map: Multimodal LLMs / vision-language models → Medical vision-language models

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 7 of its 25 references that are in the map. First posted on arXiv 7 Nov 2025.

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7 of 25 references.

References · 7

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2024GPT-4o System Cardcited by 4,588
  4. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,234
  5. 2023ShareGPT4V: Improving Large Multi-Modal Models with Better Captionscited by 1,005
  6. 2024Gemma: Open Models Based on Gemini Research and Technologycited by 916
  7. 2023Distilling Large Vision-Language Model with Out-of-Distribution Generalizabilitycited by 33

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  1. 2025DentalGPT: Incentivizing Multimodal Complex Reasoning in Dentistry
  2. 2025DentVLM: A Multimodal Vision-Language Model for Comprehensive Dental Diagnosis and Enhanced Clinical Practice
  3. 2025OralGPT-Omni: A Versatile Dental Multimodal Large Language Model
  4. 2026OralMLLM-Bench: Evaluating Cognitive Capabilities of Multimodal Large Language Models in Dental Practice
  5. 2025Towards Better Dental AI: A Multimodal Benchmark and Instruction Dataset for Panoramic X-ray Analysis
  6. 2026MetaDent: Labeling Clinical Images for Vision-Language Models in Dentistry
  7. 2025DeepSeek performs better than other Large Language Models in Dental Cases
  8. 2026Pocket-Dentist: On-Device Dental Image Understanding via Efficient Multimodal Large Language Models