arxiv: 2604.13060 · v1 · submitted 2026-03-18 · 💻 cs.CL · cs.LG· cs.MM

Recognition: no theorem link

Dental-TriageBench: Benchmarking Multimodal Reasoning for Hierarchical Dental Triage

Ziyi He , Yushi Feng , Shuangyu Yang , Yinghao Zhu , Xichen Zhang , Pak Chuen Patrick Tai , Hei Yuet Lo , Songying Wu

show 2 more authors

Weifa Yang Lequan Yu

Authors on Pith no claims yet

Pith reviewed 2026-05-15 10:18 UTC · model grok-4.3

classification 💻 cs.CL cs.LGcs.MM

keywords dental triagemultimodal reasoningclinical benchmarkreferral planningMLLM evaluationhuman-AI gaporthopantomogramhierarchical classification

0 comments

The pith

Current multimodal models fall short of junior dentists when building complete referral plans from patient complaints and X-rays.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Dental-TriageBench, a set of 246 real outpatient dental cases each paired with expert-written reasoning steps and layered referral labels. It tests 19 different multimodal models against three junior dentists and documents a clear performance difference, especially at the level of specific treatments rather than broad categories. Models frequently produce referral lists that leave out needed specialties, while humans integrate both the written complaint and the panoramic radiograph to arrive at wider, more accurate plans. The work shows that both text and image inputs are required for good results and that errors cluster on cases needing referrals across several domains. This benchmark supplies a concrete way to measure progress toward AI systems that route patients safely in dental clinics.

Core claim

Dental-TriageBench contains 246 de-identified cases drawn from authentic workflows, each annotated with expert golden reasoning trajectories and hierarchical triage labels that specify both broad categories and fine-grained treatment referrals. When 19 proprietary, open-source, and medical MLLMs are evaluated against junior-dentist baselines, models exhibit a substantial gap on treatment-level triage, producing overly narrow referral sets and omission-heavy errors precisely on cases that span multiple referral domains. Accurate performance requires joint use of complaint text and OPG images, confirming that the benchmark isolates the integration step that current models handle poorly.

What carries the argument

Dental-TriageBench, the dataset of 246 expert-annotated multimodal cases with hierarchical referral labels and golden reasoning trajectories that directly tests coverage and completeness of triage decisions.

If this is right

Accurate dental triage requires models to jointly process complaint text and radiographic images rather than relying on either modality alone.
Model errors concentrate on multi-domain cases, indicating that current systems under-generate referral options when several specialties are appropriate.
The observed human-model gap at the fine-grained treatment level points to the need for explicit coverage mechanisms in referral generation.
The benchmark supplies concrete targets for training or fine-tuning that aim to reduce omission errors in safety-critical routing.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar gaps are likely to appear in other medical specialties that combine text notes with imaging for layered referral decisions.
The expert reasoning trajectories could serve as supervision signals to train models that explicitly list all appropriate domains before selecting treatments.
Deployment tests that track downstream patient outcomes would reveal whether the benchmark's coverage metric predicts real-world safety.
Extending the hierarchical label structure to time-sensitive or resource-limited settings could expose additional failure modes not captured in outpatient data.

Load-bearing premise

The 246 de-identified cases and their expert-authored labels faithfully represent the range of decisions made in real outpatient dental triage.

What would settle it

A follow-up evaluation on several hundred fresh, live clinical cases in which one or more tested models reach or exceed the junior-dentist referral coverage rate on the same hierarchical label set.

Figures

Figures reproduced from arXiv: 2604.13060 by Hei Yuet Lo, Lequan Yu, Pak Chuen Patrick Tai, Shuangyu Yang, Songying Wu, Weifa Yang, Xichen Zhang, Yinghao Zhu, Yushi Feng, Ziyi He.

**Figure 1.** Figure 1: Multimodal dental triage. Given a patient’s multimodal information (e.g., chief complaint, orthopantomogram), the model predicts a multi-label specialty referral plan that reflects the patient’s treatment needs. fore inherently multimodal, requiring clinicians to integrate textual complaints with radiographic evidence. It is not merely a perception problem, but a multimodal clinical decision-making task … view at source ↗

**Figure 2.** Figure 2: Overview of DENTAL-TRIAGEBENCH. Steps 1–4 illustrate the dataset construction pipeline, and Step 5 summarizes the main experimental settings and key findings. increases workload, but under-referral leaves clinically relevant problems uncovered and can delay necessary care (Williams et al., 2024; Masanneck et al., 2024; Lu et al., 2024). Existing evaluations therefore do not fully test whether multimodal m… view at source ↗

**Figure 3.** Figure 3: Data statistics of DENTAL-TRIAGEBENCH. fine-grained triage labels that cover major referral destinations and treatment types in routine dental triage. The full taxonomy is provided in Appendix E. The two dentists first reviewed all cases independently and then resolved disagreements through discussion to obtain the final ground truth. 3.2 Benchmark Characteristics As shown in the bar chart of [PITH_FULL_… view at source ↗

**Figure 4.** Figure 4: CC-only: only chief complaint; OPG-only: [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 5.** Figure 5: Distribution of five clinically grounded failure [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗

**Figure 6.** Figure 6: A failure taxonomy case study on MedGemma-27B, with correct triage label on green , and missing triage label on red . Crucially, the value of multimodal input becomes even clearer at the case level. As shown in [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗

**Figure 8.** Figure 8: Distribution of the number of positive triage [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗

**Figure 9.** Figure 9: Trade-off between omission risk and over [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗

**Figure 10.** Figure 10: Confusion matrices for human–judge agreement on five failure dimensions over 60 sampled model [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗

**Figure 11.** Figure 11: Hierarchical taxonomy of the dental triage label space in our benchmark. The taxonomy is organized into two levels: 8 coarse-grained triage domains (e.g., Periodontology, Endodontics, and OMFS) and 22 fine-grained triage categories, which specify treatment-oriented referral categories within each coarse-grained triage domain. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_11.png] view at source ↗

**Figure 12.** Figure 12: A representative modality-ablation case on Gemini-3-Flash. The chief complaint alone supports Gingivitis and Exodontia, but misses the surgical complexity required for Minor O.S.. With access to the OPG, the model correctly recovers the full OMFS referral plan. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_12.png] view at source ↗

**Figure 13.** Figure 13: Additional failure case study of Gemini-3-Pro. The model misinterprets the status of tooth 36 and overestimates the severity of tooth 27 on the OPG, leading to incorrect positive predictions for Molar Endodontics and Crowns. Although it correctly identifies the impaction of tooth 48, it fails to map this finding to the appropriate OMFS referral for Exodontia. This example illustrates how OPG misinterpreta… view at source ↗

read the original abstract

Dental triage is a safety-critical clinical routing task that requires integrating multimodal clinical information (e.g., patient complaints and radiographic evidence) to determine complete referral plans. We present Dental-TriageBench, the first expert-annotated benchmark for reasoning-driven multimodal dental triage. Built from authentic outpatient workflows, it contains 246 de-identified cases annotated with expert-authored golden reasoning trajectories, together with hierarchical triage labels. We benchmark 19 proprietary, open-source, and medical-domain MLLMs against three junior dentists serving as the human baseline, and find a substantial human--model gap, on fine-grained treatment-level triage. Further analyses show that accurate triage requires both complaint and OPG information, and that model errors concentrate on cases with multiple referral domains, where MLLMs tend to produce overly narrow referral sets and omission-heavy errors. Dental-TriageBench provides a realistic testbed for developing multimodal clinical AI systems that are more clinically grounded, coverage-aware, and safer for downstream care.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Dental-TriageBench supplies the first expert-annotated multimodal test set for hierarchical dental triage and documents a clear human-model gap on multi-domain cases.

read the letter

The paper's core offering is Dental-TriageBench: 246 de-identified outpatient cases with expert-written reasoning trajectories and hierarchical triage labels. It benchmarks 19 MLLMs against three junior dentists and reports that models lag on fine-grained treatment-level decisions, especially by producing narrow referral sets and omitting domains when multiple specialties are involved. The finding that both complaint text and OPG images are required for decent performance is direct and practical. The error analysis that concentrates failures on multi-referral cases gives a concrete direction for future work. These elements make the benchmark a usable starting point for anyone testing clinical multimodal systems in dentistry. The main limitation is thin documentation on how the labels were produced and validated. The abstract and available text give no inter-rater agreement numbers, no explicit inclusion criteria for the 246 cases, and no comparison of referral-domain frequencies against clinic-wide or published dental-triage statistics. Without those checks the size of the reported gap could be sensitive to the particular sample rather than a stable property of current models. The work is aimed at researchers building or evaluating multimodal clinical AI for routing tasks. Anyone who needs a realistic testbed with reasoning traces will find it immediately useful. It deserves peer review because a properly documented benchmark in this safety-critical area can move the field forward even if the current version requires added methodological detail and statistical reporting.

Referee Report

2 major / 3 minor

Summary. The paper introduces Dental-TriageBench, the first expert-annotated benchmark for multimodal dental triage, comprising 246 de-identified outpatient cases with expert-authored golden reasoning trajectories and hierarchical triage labels. It evaluates 19 proprietary, open-source, and medical-domain MLLMs against a human baseline of three junior dentists, reporting a substantial performance gap on fine-grained treatment-level triage. Analyses indicate that accurate triage requires both complaint and OPG information, with model errors concentrating on multi-domain cases where MLLMs produce overly narrow referral sets and omission-heavy errors.

Significance. If the benchmark construction is shown to faithfully represent real outpatient workflows, the reported human-model gap and specific error patterns would provide a valuable, realistic testbed for developing safer, coverage-aware multimodal clinical AI systems. The work highlights concrete limitations in current MLLMs for integrating multimodal inputs in a safety-critical routing task and could inform targeted improvements in referral completeness.

major comments (2)

[Benchmark construction] Benchmark construction section: No inter-annotator agreement statistics, explicit case-selection criteria, or quantitative comparison of referral-domain distributions against clinic-wide or published dental-triage statistics are reported for the 246 cases and expert-authored labels. This directly undermines the central claim that the observed gap (narrow referrals and omissions on multi-domain cases) generalizes beyond this specific set rather than reflecting an artifact of the chosen distribution.
[Experiments and results] Experiments and results section: The human baseline consists of only three junior dentists with no reported error bars, confidence intervals, or statistical tests (e.g., significance of the gap on fine-grained triage metrics). This leaves the 'substantial human-model gap' finding only partially supported, especially given the small sample and the paper's emphasis on the gap as a key result.

minor comments (3)

[Abstract] Abstract: The description of hierarchical triage labels and evaluation metrics could be expanded with one sentence specifying the exact hierarchy levels and primary scoring criteria used for the gap analysis.
[Error analysis] Error analysis: The concentration of errors on multi-domain cases is noted, but a table or figure quantifying the distribution of single- vs. multi-domain cases in the benchmark (and model omission rates per domain) would improve clarity and reproducibility.
[Related work] Related work: Consider adding citations to prior multimodal medical benchmarks (e.g., in radiology or general triage) to better situate Dental-TriageBench's novelty in hierarchical referral planning.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help strengthen the presentation of Dental-TriageBench. We address each major comment below and will revise the manuscript to incorporate additional details and analyses where feasible.

read point-by-point responses

Referee: [Benchmark construction] Benchmark construction section: No inter-annotator agreement statistics, explicit case-selection criteria, or quantitative comparison of referral-domain distributions against clinic-wide or published dental-triage statistics are reported for the 246 cases and expert-authored labels. This directly undermines the central claim that the observed gap (narrow referrals and omissions on multi-domain cases) generalizes beyond this specific set rather than reflecting an artifact of the chosen distribution.

Authors: We acknowledge the absence of these details in the original submission. In revision, we will add inter-annotator agreement statistics (e.g., Cohen's kappa) computed during the expert annotation process for the hierarchical labels and reasoning trajectories. We will also expand the benchmark construction section with explicit case-selection criteria, including how the 246 de-identified outpatient cases were sampled from real workflows while preserving privacy. For referral-domain distributions, clinic-wide statistics are unavailable due to institutional data-access restrictions; however, we will include a quantitative comparison against published dental-triage studies to better support generalizability claims. These additions will directly address concerns about potential artifacts in the observed error patterns. revision: partial
Referee: [Experiments and results] Experiments and results section: The human baseline consists of only three junior dentists with no reported error bars, confidence intervals, or statistical tests (e.g., significance of the gap on fine-grained triage metrics). This leaves the 'substantial human-model gap' finding only partially supported, especially given the small sample and the paper's emphasis on the gap as a key result.

Authors: We agree that the human baseline is small and that variability measures and statistical tests were omitted. In the revised manuscript, we will report error bars (standard errors) and 95% confidence intervals for all human performance metrics on the hierarchical triage tasks. We will also conduct and report appropriate statistical tests (e.g., paired Wilcoxon signed-rank tests) to evaluate the significance of the model-human gaps, particularly on fine-grained treatment-level triage. While the sample of three junior dentists reflects practical constraints of recruiting domain experts for this safety-critical task, we will explicitly discuss this as a limitation and note that the gap remains directionally consistent across metrics. revision: yes

Circularity Check

0 steps flagged

No circularity; pure empirical benchmark with no derivations

full rationale

The paper constructs and releases an empirical benchmark dataset of 246 de-identified cases with expert-authored labels and reasoning trajectories, then directly evaluates 19 MLLMs against a human baseline of three junior dentists. No equations, fitted parameters, predictions derived from inputs, or self-citation chains appear in the derivation of any result. All reported gaps and error patterns are computed from explicit comparisons on the held-out cases. The representativeness of the 246 cases is an external-validity question, not a reduction of any claimed derivation to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

This is an empirical benchmark paper. No free parameters or invented entities are introduced. The central claim rests on the domain assumption that expert annotations constitute reliable ground truth.

axioms (1)

domain assumption Expert-authored golden reasoning trajectories and hierarchical triage labels are accurate and reliable representations of clinical decisions.
The benchmark construction, human baseline, and all reported gaps depend on this assumption about annotation quality.

pith-pipeline@v0.9.0 · 5503 in / 1302 out tokens · 59304 ms · 2026-05-15T10:18:15.262415+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

20 extracted references · 20 canonical work pages · 2 internal anchors

[1]

InThe Thirty-ninth Annual Conference on Neural Information Processing Systems

IPAD: Inverse prompt for AI detection - a robust and interpretable LLM-generated text detector. InThe Thirty-ninth Annual Conference on Neural Information Processing Systems. Zhihong Chen, Maya Varma, Justin Xu, Mag- dalini Paschali, Dave Van Veen, Andrew Johnston, Alaa Youssef, Louis Blankemeier, Christian Blueth- gen, Stephan Altmayer, and 1 others. 202...

work page arXiv 2022
[2]

InFindings of the Association for Computational Lin- guistics: EMNLP 2024, Miami, Florida, USA, Novem- ber 12-16, 2024, volume EMNLP 2024 ofFindings of ACL, pages 5747–5764

Triageagent: Towards better multi-agents collab- orations for large language model-based clinical triage. InFindings of the Association for Computational Lin- guistics: EMNLP 2024, Miami, Florida, USA, Novem- ber 12-16, 2024, volume EMNLP 2024 ofFindings of ACL, pages 5747–5764. Association for Computational Linguistics. Lars Masanneck, Linea Schmidt, Ant...

work page arXiv 2024
[3]

MedGemma Technical Report

Climedbench: A large-scale chinese benchmark for evaluating medical large language models in clini- cal scenarios. InProceedings of the 2024 Conference on Empirical Methods in Natural Language Process- ing, EMNLP 2024, Miami, FL, USA, November 12-16, 2024, pages 8428–8438. Association for Computational Linguistics. Andrew Sellergren, Sahar Kazemzadeh, Tia...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[4]

Applications of artificial intelligence in the anal- ysis of dental panoramic radiographs: an overview of systematic reviews.Dentomaxillofacial Radiology, 52(7):20230284. N. van Nistelrooij, K. E. Ghoul, T. Xi, A. Saha, S. Kem- pers, M. Cenci, B. Loomans, T. Flügge, B. van Gin- neken, and S. Vinayahalingam. 2024. Combining public datasets for automated to...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[5]

retained deciduous teeth and occasional bleeding gums with pain

Llmeval-med: A real-world clinical benchmark for medical llms with physician validation. InFind- ings of the Association for Computational Linguistics: EMNLP 2025, Suzhou, China, November 4-9, 2025, pages 4888–4914. Association for Computational Lin- guistics. Tianhong Zhou, Yin Xu, Yingtao Zhu, Chuxi Xiao, Haiyang Bian, Lei Wei, and Xuegong Zhang. 2025. ...

work page 2025
[6]

Other OMFS Consult includes the following items: Tumors, cysts, TMJ, fractures, mucosal lesions

work page
[7]

Bear in mind that there could be multiple triage/treatment labels that are relevant to the patient, meaning that the patient has multiple conditions or needs multiple treatments

work page
[8]

triage_output

Your output should be a json object that is strictly formatted to the above json format, please do not add any other text or comments to the output or add any new keys to the json object. The chief complaint: {chief_complaint} And the OPG image is: Table 5: The task insturction prompt for model inference 22 Prompt for task instruction for model inference ...

work page
[9]

The orginal model output might not contain all the keys in the json object, for those keys that are not present in the original model output, you should set the value to 0

work page
[10]

Any other free text that is not related to the key-value pairs, you should convert them to the reasoning field if that makes sense

work page
[11]

Analyze the patient's chief complaint, medical history, and OPG image

The orignial input to the model is:'You are a triage expert. Analyze the patient's chief complaint, medical history, and OPG image. Output the triage decision strictly in JSON format.' Now the original model output is: {model_output} Table 6: The prompt for standardizing model output 23 Prompt for llm as judge for failure analysis # Role You are an expert...

work page
[12]

[Patient Input]: - Chief Complaint: {chief_complaint}

work page
[13]

[Label Discrepancies] (Crucial for Evaluation): - Missing Labels (False Negatives - model failed to predict): {missing_labels} - Extra Labels (False Positives - model wrongly predicted): {extra_labels}

work page
[14]

[Ground Truth (GT)]: - GT Reasoning: {gt_reasoning}

work page
[15]

Do NOT use label discrepancies as the sole trigger for finding errors

[Model]: - Model Reasoning: {model_reasoning} # Core Evaluation Philosophy (Doctor's Workflow) **CRITICAL:** You must evaluate the model primarily based on its **[Model Reasoning]** compared to the **[GT Reasoning]**. Do NOT use label discrepancies as the sole trigger for finding errors. - Example: If the model fabricates a tooth in its reasoning, it is a...

work page
[16]

**Did the model fabricate something? -> D5.** (e.g., mentions an impacted lower wisdom tooth that isn't there, or invents a symptom)

work page
[17]

**Did the model completely ignore the core text symptom? -> D1.**

work page
[18]

**Did the model completely fail to mention an important OPG finding? -> D2.** (e.g., GT mentions generalized bone loss or caries on tooth 11, but the model reasoning never mentions it)

work page
[19]

caries into pulp

**Did the model mention the correct finding but misjudge its physical severity/nature? -> D3.** (e.g., GT says "caries into pulp", but model says "superficial caries"; GT says "severe periodontitis", but model says "mild")

work page
[20]

score", output strictly 1 or 0. For the

**Did the model perceive EVERYTHING correctly about a lesion (NO D1, D2, or D3 errors), but simply assigned the wrong final Triage label? -> ONLY THEN tag D4.** # The 5 Error Dimensions Definitions: **[Text] D1: Chief Complaint Neglect** - The model's reasoning completely ignores the core symptoms/requests in the [Patient Input]. **[OPG] D2: OPG Finding O...

work page