# OPGAgent: An Agent for Auditable Dental Panoramic X-ray Interpretation

arXiv: https://arxiv.org/abs/2603.00462 · HTML: https://mlsanity.com/paper/2603.00462/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 28 Feb 2026.

## Builds on (most cited first, 16 of 16)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation](https://mlsanity.com/paper/2206.02777/) (2022)
- [MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning](https://mlsanity.com/paper/2311.10537/) (2023)
- [MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making](https://mlsanity.com/paper/2404.15155/) (2024)
- [MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow](https://mlsanity.com/paper/2503.18968/) (2025)
- [Detecting and Evaluating Medical Hallucinations in Large Vision Language Models](https://mlsanity.com/paper/2406.10185/) (2024)
- [Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance](https://mlsanity.com/paper/2402.08680/) (2024)
- [Towards Better Dental AI: A Multimodal Benchmark and Instruction Dataset for Panoramic X-ray Analysis](https://mlsanity.com/paper/2509.09254/) (2025)
- [DENTEX: Dental Enumeration and Tooth Pathosis Detection Benchmark for Panoramic X-ray](https://mlsanity.com/paper/2305.19112/) (2023)
- [OralGPT-Omni: A Versatile Dental Multimodal Large Language Model](https://mlsanity.com/paper/2511.22055/) (2025)
- [DentalGPT: Incentivizing Multimodal Complex Reasoning in Dentistry](https://mlsanity.com/paper/2512.11558/) (2025)
- [MICCAI STS 2024 Challenge: Semi-Supervised Instance-Level Tooth Segmentation in Panoramic X-ray and CBCT Images](https://mlsanity.com/paper/2511.22911/) (2025)
- [When CNNs Outperform Transformers and Mambas: Revisiting Deep Architectures for Dental Caries Segmentation](https://mlsanity.com/paper/2511.14860/) (2025)

## Built on by (most cited first, 4 of 4)

- [OralAgent: Integrating Reasoning, Tools, and Knowledge for Interactive Dental Image Analysis](https://mlsanity.com/paper/2605.27378/) (2026)
- [Dental-TriageBench: Benchmarking Multimodal Reasoning for Hierarchical Dental Triage](https://mlsanity.com/paper/2604.13060/) (2026)
- [DentAgent: Evidence-Centric Multi-Agent Coordination for Multimodal Dental Reasoning](https://mlsanity.com/paper/2608.18878/) (2026)
- [AgenTeeth: A Model-Agnostic Framework for Suppressing Hallucination in Frozen Vision-Language Models on Dental X-Rays via Tool Evidence Injection](https://mlsanity.com/paper/2609.17800/) (2026)

## Abstract (arXiv)

Orthopantomograms (OPGs) are the standard panoramic radiograph in dentistry, used for full-arch screening across multiple diagnostic tasks. While Vision Language Models (VLMs) now allow multi-task OPG analysis through natural language, they underperform task-specific models on most individual tasks. Agentic systems that orchestrate specialized tools offer a path to both versatility and accuracy, this approach remains unexplored in the field of dental imaging. To address this gap, we propose OPGAgent, a multi-tool agentic system for auditable OPG interpretation. OPGAgent coordinates specialized perception modules with a consensus mechanism through three components: (1) a Hierarchical Evidence Gathering module that decomposes OPG analysis into global, quadrant, and tooth-level phases with dynamically invoking tools, (2) a Specialized Toolbox encapsulating spatial, detection, utility, and expert zoos, and (3) a Consensus Subagent that resolves conflicts through anatomical constraints. We further propose OPG-Bench, a structured-report protocol based on (Location, Field, Value) triples derived from real clinical reports, which enables a comprehensive review of findings and hallucinations, extending beyond the limitations of VQA indicators. On our OPG-Bench and the public MMOral-OPG benchmark, OPGAgent outperforms current dental VLMs and medical agent frameworks across both structured-report and VQA evaluation. Code will be released upon acceptance.