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
AI总结：
OralGPT-Omni是一种专门用于牙科的多模态大语言模型，通过TRACE-CoT数据集和四阶段训练范式，实现了对牙科图像的高效分析和高准确率评估。
摘要：
多模态大语言模型（MLLMs）在众多医学领域展现出了巨大的潜力；然而，牙科仍处于探索的初级阶段，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，以及可靠性挑战。在本文中，我们提出了OralGPT-Omni，这是首个专门用于牙科的MLLM，旨在对多种牙科成像模态和临床任务进行全面且可信的分析。为了明确捕捉牙医的诊断推理过程，我们构建了TRACE-CoT，一个基于临床的推理链数据集，该数据集模拟了牙科放射科医生的决策过程。这种推理监督，结合我们提出的四阶段训练范式，显著增强了模型对牙科图像的理解和分析能力。同时，我们引入了MMOral-Uni，首个统一的牙科图像分析多模态基准。它包含2,809个开放性问题-答案对，涵盖五种模态和五种任务，为数字牙科中的MLLM提供了迄今为止最全面的评估套件。OralGPT-Omni在MMOral-Uni基准上获得了51.84的总体分数，在MMOral-OPG基准上获得了45.31的分数，显著优于GPT-5的分数。我们的工作推动了智能牙科的发展，并为未来牙科图像分析的进步铺平了道路。所有代码、基准和模型都将公开发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
