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.

In recent years, large language models (LLMs) have demonstrated remarkable capabilities in the medical domain. However, existing medical benchmarks suffer from performance saturation and are predominantly derived from medical exam questions, which fail to reflect the complexity of real-world clinical scenarios.To bridge this gap, we introduce ClinBench, a challenging benchmark based on authentic clinical cases sourced from authoritative medical journals. Each question retains the complete patient information and clinical test results from the original case, effectively simulating real-world clinical practice. Additionally, we implement a rigorous human review process involving medical experts to ensure the quality and reliability of the benchmark. ClinBench supports both textual and multimodal evaluation formats, covering 11 medical specialties with over 2,000 questions, including a dedicated rare disease track, providing a comprehensive resource for assessing the medical reasoning capabilities of LLMs. We evaluate the performance of over 20 open-source and proprietary LLMs and benchmark them against human medical experts. Our findings reveal that human experts still retain an advantage within their specialized fields, while LLMs demonstrate superior overall performance on a broader range of medical specialties.