{"ID":2835747,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.22055","arxiv_id":"2511.22055","title":"OralGPT-Omni: A Versatile Dental Multimodal Large Language Model","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.","short_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 pr...","url_abs":"https://arxiv.org/abs/2511.22055","url_pdf":"https://arxiv.org/pdf/2511.22055v1","authors":"[\"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\"]","published":"2025-11-27T03:21:20Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.MM\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}