OralGPT-Omni：一种多功能的牙科多模态大语言模型
OralGPT-Omni: A Versatile Dental Multimodal Large Language Model
构建了牙科领域的思维链数据集 TRACE-CoT 和统一多模态基准 MMOral-Uni，其四阶段训练范式可迁移到其他医学影像的 VLM 微调中，为领域专用 MLLM 的可靠性评估提供了可复用的数据构造与评测思路。
原文依据 we construct TRACE-CoT, a clinically grounded chain-of-thought dataset that mirrors dental radiologists' decision-making processes
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论多模态大语言模型（MLLMs）在众多医学专科中展现出巨大潜力，然而牙科领域仍探索不足，部分原因在于领域特定数据有限、牙科专家标注稀缺、模态特定建模不足以及可靠性方面的挑战。本文提出 OralGPT-Omni，这是首个专为牙科设计的 MLLM，旨在跨多种牙科成像模态和临床任务进行全面且可信的分析。为明确捕捉牙医的诊断推理过程，我们构建了 TRACE-CoT，一个基于临床的思维链数据集，其反映了牙科放射科医生的决策过程。这种推理监督与我们提出的四阶段训练范式相结合，显著增强了模型对牙科图像理解和分析的能力。同时，我们引入了 MMOral-Uni，这是首个用于牙科图像分析的统一多模态基准。它包含 2,809 个开放式问答对，涵盖五种模态和五项任务，为数字牙科中的 MLLMs 提供了迄今为止全面的评估套件。OralGPT-Omni 在 MMOral-Uni 基准上取得 51.84 的总分，在 MMOral-OPG 基准上取得 45.31 的总分，大幅超越 GPT-5 的得分。我们的工作推动了智能牙科的发展，并为牙科图像分析的未来进步铺平了道路。所有代码、基准和模型将公开提供。
英文原文摘要 1459 字符
Multimodal Large Language Models (MLLMs) have exhibited immense potential across numerous medical specialties, yet dentistry remains underexplored, in part due to limited domain-specific data, scarce dental expert annotations, insufficient modality-specific modeling, and challenges in reliability. In this paper, we present OralGPT-Omni, the first dental-specialized MLLM designed for comprehensive and trustworthy analysis across diverse dental imaging modalities and clinical tasks. To explicitly capture dentists' diagnostic reasoning, we construct TRACE-CoT, a clinically grounded chain-of-thought dataset that mirrors dental radiologists' decision-making processes. This reasoning supervision, combined with our proposed four-stage training paradigm, substantially strengthens the model's capacity for dental image understanding and analysis. In parallel, we introduce MMOral-Uni, the first unified multimodal benchmark for dental image analysis. It comprises 2,809 open-ended question-answer pairs spanning five modalities and five tasks, offering a comprehensive evaluation suite to date for MLLMs in digital dentistry. OralGPT-Omni achieves an overall score of 51.84 on the MMOral-Uni benchmark and 45.31 on the MMOral-OPG benchmark, dramatically outperforming the scores of GPT-5. Our work promotes intelligent dentistry and paves the way for future advances in dental image analysis. All code, benchmark, and models will be made publicly available.