Work

Comparative performance analysis of AI-based large language models in assessing cervical vertebral maturation stages on lateral cephalometric radiographs

Year: 2026

Type: article

Abstract: BACKGROUND: The aim of this study was to evaluate the performance of artificial intelligence (AI)-based large language models (LLMs) in predicting cervical vertebral maturation (CVM) stages on lateral cephalometric radiographs. METHODS: This retrospective study evaluated the performance of AI-based LLMs in predicting CVM stages using 120 lateral cephalometric radiographs obtained from individuals aged 6–19 years. The radiographs, which included an equal number of samples from each CVM stage, were independently classified by two experienced orthodontists, with the consensus-established stages serving as the gold standard. Five distinct LLMs (GPT-4o, GPT-o3 pro, GPT-5, GPT-5 pro, and Grok-4) were tested in separate sessions using the same command for each image. Model performance was assessed using accuracy, correlation coefficients, Bland–Altman analysis, and mean absolute error (MAE). RESULTS: Exact-match accuracy of the AI-based LLMs ranged between 14% and 28%, while accuracy within ± 1 stage tolerance ranged from 55% to 64%. GPT-4o demonstrated the highest correlation with the reference standard (ρ = 0.616, p < 0.001), followed by GPT-5 pro (ρ = 0.535). Other AI-based LLMs exhibited moderate correlations (ρ = 0.3–0.4). Bland–Altman analyses indicated bias values close to zero but revealed wide limits of agreement. MAE values were comparable across AI-based LLMs, with no statistically significant differences (p > 0.05). CONCLUSIONS: Current LLMs did not exhibit clinically acceptable agreement with expert CVM assessments, showing wide error margins that limit their clinical utility. LLMs should presently be considered only as supportive tools. Further improvements in training and multimodal model design are needed to improve their diagnostic reliability.

Source: BMC Oral Health

DOI: 10.1186/s12903-026-08293-8

Authors: Ruşen Erdem, Ahmet Yıldırım, Yavuz Selim Genç, Büşra Beşer Gül, Muhammed Enes Naralan, Orhan Cicek

Institutions: Kafkas University, Zonguldak Bülent Ecevit University, Giresun University, Recep Tayyip Erdoğan University

Language: English

Topic: Orthodontics and Dentofacial Orthopedics

Subfield: Orthodontics

Field: Dentistry

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,190