DentalGPT: Incentivizing Multimodal Reasoning in Dentistry

Reliable interpretation of multimodal dental data is essential for automated oral healthcare, yet existing multimodal large language models (MLLMs) exhibit limited understanding of dental images. Although complex reasoning improves performance, its gains in dentistry are substantially smaller than in other medical domains, indicating 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 Dental-GPT, 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 expertannotated benchmarks and dental subsets of medical VQA benchmarks show that Dental-GPT achieves superior performance on disease classification and dental VQA tasks, outperforming many state-of-the-art MLLMs despite its compact 7B parameter scale.