DentalGPT: Incentivizing Multimodal Reasoning in Dentistry

ACL ARR 2026 January Submission8534 Authors

Keywords: Multimodal Large Language Models, AI in Dentistry, Medical Large Language Models

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.

Paper Type: Long

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Research Area Keywords: vision question answering, cross-modal application, multimodality

Languages Studied: English

Submission Number: 8534