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

Zhaolin Yu, Litao Yang, Ben Babicka, Ming Hu, Jing Hao, Anthony Huang, James Huang, Yueming Jin, Jiasong Wu, Zongyuan Ge

TL;DR

OPGAgent, a multi-tool agentic system for auditable OPG interpretation that outperforms current dental VLMs and medical agent frameworks across both structured-report and VQA evaluation.

Abstract

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.

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

TL;DR

OPGAgent, a multi-tool agentic system for auditable OPG interpretation that outperforms current dental VLMs and medical agent frameworks across both structured-report and VQA evaluation.

Abstract

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.
Paper Structure (10 sections, 1 equation, 2 figures, 3 tables)

This paper contains 10 sections, 1 equation, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. OPGAgent
  3. Overview.
  4. Hierarchical Evidence Gathering
  5. Specialized Toolbox
  6. Consensus Subagent
  7. Structured Reporting and Protocol Design
  8. Hierarchical Clinical Ontology.
  9. Experiments
  10. Conclusion

Figures (2)

  • Figure 1: Overview of OPGAgent. The Agent orchestrates three modules: Hierarchical Evidence Gathering, Specialized Toolbox, and Consensus Subagent.
  • Figure 2: Vision comparison. OPGAgent correctly identifies missing teeth, bone loss, and impacted tooth 38 via consensus, while GPT-5.2 and Gemini-3-Flash each miss or hallucinate findings.Blue for correct; Orange for wrong; Green for all undetected.