OralGPT-Omni: A Versatile Dental Multimodal Large Language Model
Jing Hao, Yuci Liang, Lizhuo Lin, Yuxuan Fan, Wenkai Zhou, Kaixin Guo, Zanting Ye, Yanpeng Sun, Xinyu Zhang, Yanqi Yang, Qiankun Li, Hao Tang, James Kit-Hon Tsoi, Linlin Shen, Kuo Feng Hung

TL;DR
OralGPT-Omni is a novel dental multimodal large language model designed for comprehensive analysis across various dental imaging modalities, utilizing a new reasoning dataset and benchmark to improve dental image understanding and diagnostics.
Contribution
The paper introduces OralGPT-Omni, the first specialized multimodal LLM for dentistry, along with TRACE-CoT dataset and MMOral-Uni benchmark to enhance and evaluate dental image analysis capabilities.
Findings
OralGPT-Omni outperforms GPT-5 on dental benchmarks.
The model achieves 51.84 on MMOral-Uni and 45.31 on MMOral-OPG.
The approach significantly improves dental image understanding and diagnostic reasoning.
Abstract
Multimodal Large Language Models (MLLMs) have exhibited immense potential across numerous medical specialties; yet, dentistry remains underexplored, in part due to limited domain-specific data, scarce dental expert annotations, insufficient modality-specific modeling, and challenges in reliability. In this paper, we present OralGPT-Omni, the first dental-specialized MLLM designed for comprehensive and trustworthy analysis across diverse dental imaging modalities and clinical tasks. To explicitly capture dentists' diagnostic reasoning, we construct TRACE-CoT, a clinically grounded chain-of-thought dataset that mirrors dental radiologists' decision-making processes. This reasoning supervision, combined with our proposed four-stage training paradigm, substantially strengthens the model's capacity for dental image understanding and analysis. In parallel, we introduce MMOral-Uni, the first…
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Taxonomy
TopicsArtificial Intelligence in Healthcare and Education · Dental Radiography and Imaging · Topic Modeling
