@inproceedings{cai-etal-2026-dentalgpt,
    title = "{D}ental{GPT}: Incentivizing Multimodal Reasoning in Dentistry",
    author = "Cai, Zhenyang  and
      Zhang, Jiaming  and
      Zhao, Junjie  and
      Zeng, Ziyi  and
      Li, Yanchao  and
      Jingyi, Liang  and
      Chen, Junying  and
      Yang, Yunjin  and
      You, Jiajun  and
      Deng, Shuzhi  and
      Xieruiqiii  and
      Chen, Yuanting  and
      Feng, Xiangyi  and
      Li, Jianquan  and
      Chen, Liangyi  and
      Wang, Junwen  and
      Jiang, Shan  and
      Wang, Benyou",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.135/",
    pages = "2811--2829",
    ISBN = "979-8-89176-395-1",
    abstract = "Reliable interpretation of multimodal dental data is essential for automated oral healthcare, yet current multimodal large language models (MLLMs) show limited understanding of dental images. Although complex reasoning improves performance, its gains in dentistry are substantially smaller than in other medical domains, suggesting that complex reasoning is not yet sufficiently incentivized for dental diagnosis, likely due to insufficient domain knowledge and limited reinforcement learning on dental questions. We present DentalGPT, a dentistry-specialized MLLM trained via staged multimodal alignment and reinforcement learning. By constructing the largest annotated multimodal dental dataset to date with over 120k images, multimodal alignment provides the necessary domain knowledge foundation to support and incentivize complex reasoning, which is further strengthened through reinforcement learning. Experiments on expert-annotated benchmarks and dental subsets of medical VQA benchmarks show that DentalGPT achieves superior performance on disease classification and dental VQA tasks, outperforming many state-of-the-art MLLMs despite its compact 7B parameter scale."
}