OPGAgent: An Agent for Auditable Dental Panoramic X-ray Interpretation
Authors: Zhaolin Yu, Litao Yang, Ben Babicka, Ming Hu, Jing Hao, Anthony Huang, James Huang, Yueming Jin, +2 more
Organizations: AIM for Health Lab, Faculty of Information Technology, Monash University, Melbourne, Australia · Faculty of Information Technology, Monash University, Melbourne, Australia · Curae Health, Melbourne, Australia · Faculty of Dentistry, The University of Hong Kong, Hong Kong · Airdoc-Monash Research, Monash University, VIC 3800, Australia · National University of Singapore, Singapore City, Singapore · Southeast University, Nanjing, China
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.
Figures & tables
Figure 1: Overview of OPGAgent. The Agent orchestrates three modules: Hierarchical Evidence Gathering, Specialized Toolbox, and Consensus Subagent.
Methods
E.M.
Det.
Loc.
Cls.
Score
P
R
F1
FP ↓
P
R
F1
P
R
F1
Acc
Gemini-3-Flash
0.428
0.276
0.451
0.343
10.58
0.440
0.596
0.506
0.311
0.488
0.380
0.796
Kimi-k2.5
0.399
0.252
0.345
0.291
9.20
0.481
0.500
0.490
0.348
0.463
0.397
0.764
Gemini-3-Pro
0.399
0.323
0.294
0.308
5.60
0.556
0.431
0.485
0.458
0.319
0.376
0.732
GPT-5.2
0.357
0.283
0.273
0.278
6.17
0.624
0.359
0.456
0.392
0.193
0.258
0.754
Qwen3-VL-235B
0.230
0.106
0.191
0.137
14.50
0.316
0.436
0.366
0.107
0.175
0.133
0.614
Table 1: Quantitative comparison on OPG-Bench dataset.
Methods
MMOral-OPG
OPG-Bench (VQA)
Acc.
Tee.
Pat.
Jaw
HisT
Acc.
Mis.
Muc.
Bone
Anat.
Api.
Gemini-3-Flash
48.47%
54.1%
42.6%
59.7%
66.2%
59.9%
48.4%
47.9%
60.0%
65.1%
42.2%
GPT-5.2
43.58%
37.2%
32.4%
65.9%
52.1%
51.5%
48.8%
44.7%
50.5%
65.1%
27.7%
OralGPT-Omni
59.06%
53.5%
44.6%
77.5%
69.7%
36.1%
13.6%
15.7%
23.2%
30.8%
17.0%
DentalGPT
48.27%
42.3%
40.5%
65.1%
47.2%
27.8%
11.4%
22.2%
4.2%
13.0%
17.5%
OPGAgent
62.53%
59.7%
52.7%
72.1%
71.8%
61.2%
67.0%
65.2%
76.8%
80.8%
35.0%
Table 2: Performance comparison on VQA benchmarks.
Components
Metrics
Expert Zoos
Spatial Tools
Detection Tools
Precision
Recall
F1
FP/Case ↓
✗
✗
✗
28.32%
27.25%
27.78%
6.17
✓
✗
✗
38.62%
16.64%
23.25%
2.37
✓
✓
✗
37.13%
35.98%
36.55%
5.47
✓
✓
✓
43.15%
41.48%
42.30%
4.89
Table 3: Ablation study on OPG-Bench with cumulative component additions.
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.
The Hong Kong University of Science and Technology (GZ) · School of Computer Science, Peking University · Faculty of Dentistry, The University of Hong Kong +3
Faculty of Dentistry, The University of Hong Kong, Hong Kong, China · Department of Computing, Imperial College London, London, United Kingdom · University of Science and Technology of China, Hefei, China +2