@inproceedings{he-etal-2026-dental,
title = "Dental-{T}riage{B}ench: Benchmarking Multimodal Reasoning for Hierarchical Dental Triage",
author = "He, Ziyi and
Feng, Yushi and
Yang, Shuangyu and
Zhu, Yinghao and
Zhang, Xichen and
Patrick, Tai Pak Chuen and
Yuet, Lo Hei and
Wu, Songying and
Yang, Weifa and
Yu, Lequan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.174/",
pages = "3987--4012",
ISBN = "979-8-89176-459-0",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Dental-TriageBench: Benchmarking Multimodal Reasoning for Hierarchical Dental Triage
%A He, Ziyi
%A Feng, Yushi
%A Yang, Shuangyu
%A Zhu, Yinghao
%A Zhang, Xichen
%A Patrick, Tai Pak Chuen
%A Yuet, Lo Hei
%A Wu, Songying
%A Yang, Weifa
%A Yu, Lequan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F he-etal-2026-dental
%X 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.
%U https://aclanthology.org/2026.emnlp-main.174/
%P 3987-4012
Markdown (Informal)
[Dental-TriageBench: Benchmarking Multimodal Reasoning for Hierarchical Dental Triage](https://aclanthology.org/2026.emnlp-main.174/) (He et al., EMNLP 2026)
ACL
- Ziyi He, Yushi Feng, Shuangyu Yang, Yinghao Zhu, Xichen Zhang, Tai Pak Chuen Patrick, Lo Hei Yuet, Songying Wu, Weifa Yang, and Lequan Yu. 2026. Dental-TriageBench: Benchmarking Multimodal Reasoning for Hierarchical Dental Triage. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 3987–4012, Budapest, Hungary. Association for Computational Linguistics.