OralGPT-Plus: Learning to Use Visual Tools via Reinforcement Learning for Panoramic X-ray Analysis

TL;DR

OralGPT-Plus is a reinforcement learning-based vision-language model that performs iterative, symmetry-aware reasoning for panoramic dental X-ray analysis, improving diagnostic accuracy and reliability.

Contribution

The paper introduces OralGPT-Plus, a novel agentic model with a new dataset and benchmark for interactive, symmetry-aware panoramic dental radiograph diagnosis.

Findings

OralGPT-Plus outperforms strong baselines on MMOral-X and other benchmarks.

The model demonstrates improved stability and accuracy in long-horizon reasoning.

Reinforcement learning with structured rewards enhances diagnostic performance.

Abstract

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.