让AI像医生一样反复检查全景牙片，提升诊断准确性。
OralGPT-Plus: Learning to Use Visual Tools via Reinforcement Learning for Panoramic X-ray Analysis
- 用强化学习驱动迭代检查，关注牙齿左右对称性。
- 在300个开放问题上表现优于现有模型，准确率提升12.6%。
- 适合医疗AI研究者和临床辅助诊断系统开发者。
全景牙科影像需要精细的空间推理、双侧对称理解及多步诊断验证，但现有视觉语言模型采用静态单次处理范式，限制了临床可靠性。本文提出OralGPT-Plus，一种用于全景牙片分析的代理型视觉语言模型，支持迭代与对称感知的诊断推理。为此，我们构建了包含五千张图像的DentalProbe数据集，提供专家标注的诊断轨迹，实现局部检查与对侧对比的结构化监督。同时，设计基于评分表与诊断驱动的强化学习框架，促进临床有意义的重检，并稳定长时程推理。此外，提出首个全景诊断基准MMOral-X，包含300个开放式问题及多难度级别的区域级标注。OralGPT-Plus在MMOral-X及主流全景基准上持续优于强基线，证明交互式与对称性感知推理的有效性。本工作凸显代理建模在牙科影像中的价值，并为未来临床对齐的全景影像分析研究奠定基础。
原文摘要 · Abstract (English)
Panoramic dental radiographs require fine-grained spatial reasoning, bilateral symmetry understanding, and multi-step diagnostic verification, yet existing vision-language models operate under a static single-pass paradigm that limits their clinical reliability. In this paper, we introduce OralGPT-Plus, an agentic vision-language model designed to perform iterative and symmetry-aware diagnostic reasoning for panoramic dental radiograph analysis. To support this paradigm, we construct DentalProbe, a five-thousand-image dataset with expert-curated diagnostic trajectories that provide structured supervision for localized inspection and contralateral comparison. We further develop a Reinspection-driven reinforcement learning framework that encourages clinically meaningful re-examination and stabilizes long-horizon reasoning with rubric-based reward and conditioned diagnostic-driven reward. In parallel, we present MMOral-X, the first benchmark for holistic panoramic diagnosis, containing 300 open-ended questions and region-level annotations across multiple difficulty levels. OralGPT-Plus demonstrates consistent and reliable improvements over strong baselines on MMOral-X and established panoramic benchmarks, indicating the effectiveness of interactive and symmetry-informed reasoning. Our work highlights the value of agentic modeling for dental imaging and provides a foundation for future research in clinically aligned panoramic radiograph analysis.
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