Academic Journal
Assessment of Large Language Models and Expert Clinicians in Grading Mandibular Third Molar Extraction Difficulty.
| Τίτλος: | Assessment of Large Language Models and Expert Clinicians in Grading Mandibular Third Molar Extraction Difficulty. |
|---|---|
| Συγγραφείς: | Yoon SM; Department of Oral and Maxillofacial Surgery, Daejeon Dental Hospital, Wonkwang University College of Dentistry, Daejeon, Republic of Korea., Yoo JH; Department of Oral and Maxillofacial Surgery, Daejeon Dental Hospital, Wonkwang University College of Dentistry, Daejeon, Republic of Korea., Kim T; Department of Oral and Maxillofacial Surgery, Daejeon Dental Hospital, Wonkwang University College of Dentistry, Daejeon, Republic of Korea., Kang JH; Department of Oral and Maxillofacial Surgery, Daejeon Dental Hospital, Wonkwang University College of Dentistry, Daejeon, Republic of Korea., Kim BC; Department of Oral and Maxillofacial Surgery, Daejeon Dental Hospital, Wonkwang University College of Dentistry, Daejeon, Republic of Korea. Electronic address: bck@wku.ac.kr. |
| Πηγή: | International dental journal [Int Dent J] 2026 Oct; Vol. 76 (5), pp. 109736. Date of Electronic Publication: 2026 Jul 06. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Country of Publication: England NLM ID: 0374714 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1875-595X (Electronic) Linking ISSN: 00206539 NLM ISO Abbreviation: Int Dent J Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2021- : London : Elsevier Original Publication: London : FDI World Dental |
| Ιατρικοί όροι (MeSH): | Molar, Third*/surgery , Molar, Third*/diagnostic imaging , Large Language Models* , Tooth Extraction*, Mandible/surgery ; Humans ; Retrospective Studies ; Cross-Sectional Studies ; Observer Variation ; Reproducibility of Results ; Radiography, Panoramic ; Female |
| Περίληψη: | Background: The Pederson difficulty score (PDS) is widely used to assess mandibular third molar extraction difficulty, but is subject to interobserver variability because no universally accepted reference standard exists. This study investigates whether multimodal large language models (LLMs) demonstrate systematic grading tendencies comparable to those of human clinicians in the absence of a definitive standard. Methods: This retrospective cross-sectional study included 100 panoramic radiographs. Two LLMs (GPT and Gemini) and two oral and maxillofacial surgeons independently graded extraction difficulty using the PDS. No reference standard or gold-standard rater was designated; all raters were treated as independent assessors. Agreement was assessed using quadratic-weighted kappa and intraclass correlation coefficient values. Systematic bias was analysed using Bland-Altman plots and ordinal logistic regression. The effects of prompt language and session protocol were also evaluated. Results: The interexpert agreement was substantial (κ = 0.754). GPT demonstrated moderate agreement with experts (κ = 0.564-0.590), whereas Gemini exhibited fair-to-moderate agreement (κ = 0.356-0.461). Overall, the four-rater reliability was moderate (intraclass correlation coefficient = 0.553). Both LLMs displayed minimal systematic bias (GPT: -0.13; Gemini: -0.08), with no significant differences in grading tendencies compared with human experts (P > .05). However, sequential-session evaluation significantly reduced the scoring consistency for both models (GPT: P = .001; Gemini: P < .001), whereas prompt language had no significant effect on total PDS scores (GPT: P = .543; Gemini: P = .386). A component-level linguistic variation was observed only in GPT's assessment of the ramus relationship (P = .003). Conclusion: The examined LLMs showed grading tendencies partly comparable to those of human clinicians, without evidence of systematic directional bias; however, agreement remained below interexpert levels and was sensitive to conversational context. These findings should not be interpreted as evidence of diagnostic accuracy or clinical reliability, as no reference standard was established. Clinical Relevance: LLMs may provide supplementary grading information if used with independent-session protocols; however, their role in clinical decision-making requires further validation against defined outcome standards. (Copyright © 2026 The Authors. Published by Elsevier Inc. All rights reserved.) |
| Competing Interests: | Conflict of interest The authors declare that they have no competing interests. |
| Contributed Indexing: | Keywords: Artificial intelligence; Decision making; Large language models; Third molar; Tooth extraction |
| Entry Date(s): | Date Created: 20260706 Date Completed: 20260916 Latest Revision: 20260916 |
| Update Code: | 20260916 |
| PubMed Central ID: | PMC13351125 |
| DOI: | 10.1016/j.identj.2026.109736 |
| PMID: | 42407142 |
| Βάση Δεδομένων: | MEDLINE |
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