Towards Better Dental AI: A Multimodal Benchmark and Instruction Dataset
  for Panoramic X-ray Analysis
Authors: name: Jing Hao, name: Yuxuan Fan, name: Yanpeng Sun, name: Kaixin Guo, name: Lizhuo Lin, name: Jinrong Yang, name: Qi Yong H. Ai, name: Lun M. Wong, name: Hao Tang, name: Kuo Feng Hung
Organization: OralGPT-Family

Recent advances in large vision-language models (LVLMs) have demonstrated
strong performance on general-purpose medical tasks. However, their
effectiveness in specialized domains such as dentistry remains underexplored.
In particular, panoramic X-rays, a widely used imaging modality in oral
radiology, pose interpretative challenges due to dense anatomical structures
and subtle pathological cues, which are not captured by existing medical
benchmarks or instruction datasets. To this end, we introduce MMOral, the first
large-scale multimodal instruction dataset and benchmark tailored for panoramic
X-ray interpretation. MMOral consists of 20,563 annotated images paired with
1.3 million instruction-following instances across diverse task types,
including attribute extraction, report generation, visual question answering,
and image-grounded dialogue. In addition, we present MMOral-Bench, a
comprehensive evaluation suite covering five key diagnostic dimensions in
dentistry. We evaluate 64 LVLMs on MMOral-Bench and find that even the
best-performing model, i.e., GPT-4o, only achieves 41.45% accuracy, revealing
significant limitations of current models in this domain. To promote the
progress of this specific domain, we also propose OralGPT, which conducts
supervised fine-tuning (SFT) upon Qwen2.5-VL-7B with our meticulously curated
MMOral instruction dataset. Remarkably, a single epoch of SFT yields
substantial performance enhancements for LVLMs, e.g., OralGPT demonstrates a
24.73% improvement. Both MMOral and OralGPT hold significant potential as a
critical foundation for intelligent dentistry and enable more clinically
impactful multimodal AI systems in the dental field. The dataset, model,
benchmark, and evaluation suite are available at
https://github.com/isbrycee/OralGPT.