{"slug": "pandent-toward-comprehensive-tooth-level-structure-language-consistency-in", "title": "PanDent: Toward Comprehensive Tooth-Level Structure-Language Consistency in Dental Radiology", "summary": "Researchers introduced PanDent, a benchmark of 9,524 expert-validated dental panoramic radiographs, and found that current multimodal large language models (MLLMs) produce fluent reports but fail to achieve clinically consistent tooth-level diagnosis, with fine-tuning on PanDent significantly improving structure-language consistency and diagnostic accuracy.", "body_md": "arXiv:2607.27378v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/pandent-toward-comprehensive-tooth-level-structure-language-consistency-in", "canonical_source": "https://arxiv.org/abs/2607.27378", "published_at": "2026-07-31 04:00:00+00:00", "updated_at": "2026-07-31 04:39:01.355019+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-research", "ai-ethics"], "entities": ["PanDent", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/pandent-toward-comprehensive-tooth-level-structure-language-consistency-in", "markdown": "https://wpnews.pro/news/pandent-toward-comprehensive-tooth-level-structure-language-consistency-in.md", "text": "https://wpnews.pro/news/pandent-toward-comprehensive-tooth-level-structure-language-consistency-in.txt", "jsonld": "https://wpnews.pro/news/pandent-toward-comprehensive-tooth-level-structure-language-consistency-in.jsonld"}}