DentalGPT: Incentivizing Multimodal Complex Reasoning in Dentistry
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918246452559872 |
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| author | Cai, Zhenyang Zhang, Jiaming Zhao, Junjie Zeng, Ziyi Li, Yanchao Liang, Jingyi Chen, Junying Yang, Yunjin You, Jiajun Deng, Shuzhi Wang, Tongfei Chen, Wanting Hao, Chunxiu Xie, Ruiqi Wen, Zhenwei Feng, Xiangyi Ting, Zou Lin, Jin Zou Li, Jianquan Yu, Guangjun Chen, Liangyi Wang, Junwen Jiang, Shan Wang, Benyou |
| author_facet | Cai, Zhenyang Zhang, Jiaming Zhao, Junjie Zeng, Ziyi Li, Yanchao Liang, Jingyi Chen, Junying Yang, Yunjin You, Jiajun Deng, Shuzhi Wang, Tongfei Chen, Wanting Hao, Chunxiu Xie, Ruiqi Wen, Zhenwei Feng, Xiangyi Ting, Zou Lin, Jin Zou Li, Jianquan Yu, Guangjun Chen, Liangyi Wang, Junwen Jiang, Shan Wang, Benyou |
| contents | Reliable interpretation of multimodal data in dentistry is essential for automated oral healthcare, yet current multimodal large language models (MLLMs) struggle to capture fine-grained dental visual details and lack sufficient reasoning ability for precise diagnosis. To address these limitations, we present DentalGPT, a specialized dental MLLM developed through high-quality domain knowledge injection and reinforcement learning. Specifically, the largest annotated multimodal dataset for dentistry to date was constructed by aggregating over 120k dental images paired with detailed descriptions that highlight diagnostically relevant visual features, making it the multimodal dataset with the most extensive collection of dental images to date. Training on this dataset significantly enhances the MLLM's visual understanding of dental conditions, while the subsequent reinforcement learning stage further strengthens its capability for multimodal complex reasoning. Comprehensive evaluations on intraoral and panoramic benchmarks, along with dental subsets of medical VQA benchmarks, show that DentalGPT achieves superior performance in disease classification and dental VQA tasks, outperforming many state-of-the-art MLLMs despite having only 7B parameters. These results demonstrate that high-quality dental data combined with staged adaptation provides an effective pathway for building capable and domain-specialized dental MLLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11558 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DentalGPT: Incentivizing Multimodal Complex Reasoning in Dentistry Cai, Zhenyang Zhang, Jiaming Zhao, Junjie Zeng, Ziyi Li, Yanchao Liang, Jingyi Chen, Junying Yang, Yunjin You, Jiajun Deng, Shuzhi Wang, Tongfei Chen, Wanting Hao, Chunxiu Xie, Ruiqi Wen, Zhenwei Feng, Xiangyi Ting, Zou Lin, Jin Zou Li, Jianquan Yu, Guangjun Chen, Liangyi Wang, Junwen Jiang, Shan Wang, Benyou Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Reliable interpretation of multimodal data in dentistry is essential for automated oral healthcare, yet current multimodal large language models (MLLMs) struggle to capture fine-grained dental visual details and lack sufficient reasoning ability for precise diagnosis. To address these limitations, we present DentalGPT, a specialized dental MLLM developed through high-quality domain knowledge injection and reinforcement learning. Specifically, the largest annotated multimodal dataset for dentistry to date was constructed by aggregating over 120k dental images paired with detailed descriptions that highlight diagnostically relevant visual features, making it the multimodal dataset with the most extensive collection of dental images to date. Training on this dataset significantly enhances the MLLM's visual understanding of dental conditions, while the subsequent reinforcement learning stage further strengthens its capability for multimodal complex reasoning. Comprehensive evaluations on intraoral and panoramic benchmarks, along with dental subsets of medical VQA benchmarks, show that DentalGPT achieves superior performance in disease classification and dental VQA tasks, outperforming many state-of-the-art MLLMs despite having only 7B parameters. These results demonstrate that high-quality dental data combined with staged adaptation provides an effective pathway for building capable and domain-specialized dental MLLMs. |
| title | DentalGPT: Incentivizing Multimodal Complex Reasoning in Dentistry |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2512.11558 |