Orthodontic Biomechanical Reasoning with Multimodal Language Models: Performance and Clinical Utility

Basit Öğe Kaydı
dc.authorid0009-0004-8920-8605
dc.authorid0000-0003-4037-9783
dc.authorid0000-0001-6152-6178
dc.contributor.authorArisan, Arda
dc.contributor.authorGenc, Celal
dc.contributor.authorDuran, Gokhan Serhat
dc.date.accessioned2026-02-03T12:00:01Z
dc.date.available2026-02-03T12:00:01Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractBackground: Multimodal large language models (LLMs) are increasingly being explored as clinical support tools, yet their capacity for orthodontic biomechanical reasoning has not been systematically evaluated. This retrospective study assessed their ability to analyze treatment mechanics and explored their potential role in supporting orthodontic decision-making. Methods: Five publicly available models (GPT-o3, Claude 3.7 Sonnet, Gemini 2.5 Pro, GPT-4.0, and Grok) analyzed 56 standardized intraoral photographs illustrating a diverse range of active orthodontic force systems commonly encountered in clinical practice. Three experienced orthodontists independently scored the outputs across four domains-observation, interpretation, biomechanics, and confidence-using a 5-point scale. Inter-rater agreement and consistency were assessed, and statistical comparisons were made between models. Results: GPT-o3 achieved the highest composite score (3.34/5.00; 66.8%), significantly outperforming all other models. The performance ranking was followed by Claude (57.8%), Gemini (52.6%), GPT-4.0 (48.8%), and Grok (38.8%). Inter-rater reliability among the expert evaluators was excellent, with ICC values ranging from 0.786 (Confidence Evaluation) to 0.802 (Observation). Model self-reported confidence showed poor calibration against expert-rated output quality. Conclusions: Multimodal LLMs show emerging potential for assisting orthodontic biomechanical assessment. With expert-guided validation, these models may contribute meaningfully to clinical decision support across diverse biomechanical scenarios encountered in routine orthodontic care.
dc.identifier.doi10.3390/bioengineering12111165
dc.identifier.issn2306-5354
dc.identifier.issue11
dc.identifier.pmid41301121
dc.identifier.scopus2-s2.0-105023123253
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.3390/bioengineering12111165
dc.identifier.urihttps://hdl.handle.net/20.500.12428/34484
dc.identifier.volume12
dc.identifier.wosWOS:001624013900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofBioengineering-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260130
dc.subjectorthodontics
dc.subjectorthodontic biomechanics
dc.subjectmultimodal large language models
dc.subjectbiomechanical reasoning
dc.subjectclinical decision support
dc.subjectAI in dental engineering
dc.titleOrthodontic Biomechanical Reasoning with Multimodal Language Models: Performance and Clinical Utility
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu