OralGPT-Omni: A Versatile Dental Multimodal Large Language Model
Authors: name: Jing Hao; name: Yuci Liang; name: Lizhuo Lin; name: Yuxuan Fan; name: Wenkai Zhou; name: Kaixin Guo; name: Zanting Ye; name: Yanpeng Sun; name: Xinyu Zhang; name: Yanqi Yang; name: Qiankun Li; name: Hao Tang; name: James Kit-Hon Tsoi; name: Linlin Shen; name: Kuo Feng Hung
Published: 2025-11-27 03:21:20

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 unified multimodal benchmark for dental image analysis. It comprises 2,809 open-ended question-answer pairs spanning five modalities and five tasks, offering a comprehensive evaluation suite to date for MLLMs in digital dentistry. OralGPT-Omni achieves an overall score of 51.84 on the MMOral-Uni benchmark and 45.31 on the MMOral-OPG benchmark, dramatically outperforming the scores of GPT-5. Our work promotes intelligent dentistry and paves the way for future advances in dental image analysis. All code, benchmark, and models will be made publicly available.