PanDent: Toward Comprehensive Tooth-Level Structure-Language Consistency in Dental Radiology

• URL: http://arxiv.org/abs/2607.27378v1
• Date: Wed, 29 Jul 2026 18:36:07 GMT
• Title: PanDent: Toward Comprehensive Tooth-Level Structure-Language Consistency in Dental Radiology
• Abstract summary: PanDent is a large-scale, clinically grounded OPG benchmark built upon fine-grained, expert-validated tooth-level annotations.<n>The dataset comprises 9,524 high-quality OPGs, each associated with comprehensive structured annotations.<n>Results show that current MLLMs can generate fluent reports, yet fail to produce clinically consistent descriptions.
• Score: 46.36925477064605
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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