PanDent: Toward Comprehensive Tooth-Level Structure-Language Consistency in Dental Radiology
Researchers introduce PanDent, a large-scale benchmark of 9,524 panoramic dental X-rays with expert-validated tooth-level annotations, to evaluate multimodal LLMs' clinical reasoning. Current models produce fluent reports but often fail at precise tooth localization and diagnosis; fine-tuning on PanDent significantly improves structure-language consistency and diagnostic accuracy.
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[Submitted on 29 Jul 2026]
Title:PanDent: Toward Comprehensive Tooth-Level Structure-Language Consistency in Dental Radiology
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Abstract:Accurate evaluation of multimodal large language models (MLLMs) in dental panoramic radiography (orthopantomogram, OPG) is limited by the lack of fine-grained, clinically reliable benchmarks that reflect expert interpretation. This work introduces PanDent, a large-scale, clinically grounded OPG benchmark built upon fine-grained, expert-validated tooth-level annotations. The dataset comprises 9,524 high-quality OPGs, each associated with comprehensive structured annotations produced by experienced dentists and further validated by an oral and maxillofacial radiologist, providing clinically reliable supervision for tooth-level diagnosis and reasoning. Clinically consistent radiology reports are constructed from expert-validated findings using clinician-defined reporting logic, establishing explicit correspondence between structured clinical evidence and free-text descriptions. This design enables evaluation of whether MLLMs generate reports that are not only linguistically coherent but also clinically consistent with expert-validated tooth-level findings. Experiments are conducted on diverse MLLMs, including state-of-the-art (SOTA) proprietary models, general-domain open-source models, and medical-specific models. Results show that current MLLMs can generate fluent reports, yet fail to produce clinically consistent descriptions, exhibiting substantial errors in fine-grained localization and tooth-level diagnosis. Fine-tuning on PanDent significantly improves structure-language consistency, substantially enhancing visual localization accuracy and diagnostic correctness, and bringing model outputs closer to expert dental interpretation. These results establish PanDent as a rigorous benchmark for evaluating tooth-level clinical reasoning in MLLMs and a valuable resource for clinically grounded dental AI.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
Cite as: arXiv:2607.27378 [cs.CV]
(or arXiv:2607.27378v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.27378
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xiaohan Li [view email] [v1] Wed, 29 Jul 2026 18:36:07 UTC (4,247 KB)
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