Towards Better Dental AI: A Multimodal Benchmark and Instruction Dataset
  for Panoramic X-ray Analysis
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.
68c3e9d1fc1747b912403b63
Jing Hao
claimed_verified
2025-09-12T16:07:11.667Z
6433a530b0dee98d17844636
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Jing Hao
user
Bryceee
68c3e9d1fc1747b912403b64
Yuxuan Fan
claimed_verified
2025-09-13T14:56:15.064Z
658247c592b5a9664de63882
https://cdn-avatars.huggingface.co/v1/production/uploads/658247c592b5a9664de63882/Jn03voLQjDlB3YjpSi-PI.jpeg
fan
user
EasonFan
68c3e9d1fc1747b912403b65
Yanpeng Sun
68c3e9d1fc1747b912403b66
Kaixin Guo
68c3e9d1fc1747b912403b67
Lizhuo Lin
68c3e9d1fc1747b912403b68
Jinrong Yang
68c3e9d1fc1747b912403b69
Qi Yong H. Ai
68c3e9d1fc1747b912403b6a
Lun M. Wong
68c3e9d1fc1747b912403b6b
Hao Tang
68c3e9d1fc1747b912403b6c
Kuo Feng Hung
Towards Better Dental AI: A Multimodal Benchmark and Instruction Dataset
  for Panoramic X-ray Analysis
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.
A new dataset and benchmark, MMOral, and a fine-tuned model, OralGPT, address the challenges of interpreting panoramic X-rays in dentistry, showing significant performance improvements over existing large vision-language models.
large vision-language models LVLMs panoramic X-rays multimodal instruction dataset attribute extraction report generation visual question answering image-grounded dialogue MMOral-Bench supervised fine-tuning SFT Qwen2.5-VL-7B intelligent dentistry