OralGPT-Omni: A Versatile Dental Multimodal Large Language Model
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.
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Jing Hao
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2025-12-02T08:49:48.611Z
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Jing Hao
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Yuci Liang
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Lizhuo Lin
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Yuxuan Fan
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2025-12-01T09:10:28.730Z
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fan
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EasonFan
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Wenkai Zhou
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Kaixin Guo
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Zanting Ye
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Yanpeng Sun
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2026-01-09T15:47:43.259Z
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yanpeng_sun
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Xinyu Zhang
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Yanqi Yang
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Qiankun Li
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Hao Tang
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James Kit-Hon Tsoi
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Linlin Shen
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Kuo Feng Hung
OralGPT-Omni: A Versatile Dental Multimodal Large Language Model
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.
OralGPT-Omni, a dental-specialized multimodal large language model, enhances dental image analysis through TRACE-CoT reasoning supervision and achieves superior performance on MMOral-Uni benchmark compared to GPT-5.
Multimodal Large Language Models MLLMs dental-specialized MLLM TRACE-CoT chain-of-thought dataset four-stage training paradigm unified multimodal benchmark dental image analysis MMOral-Uni MMOral-OPG intelligent dentistry