ArticleBMC oral health2025
Customized GPT-4V(ision) for radiographic diagnosis: can large language model detect supernumerary teeth?
Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The performance of large language models in dentomaxillofacial radiology: a systematic review.Dento maxillo facial radiology · 2025Pooled it
- Can vision-language models estimate patient age? evidence from panoramic radiographs.Clinical oral investigations · 2026Article
- Comparative Evaluation of Large Language Models for Reporting Jaw Lesions on Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Diagnostic and localization performance of multimodal large language models in the interpretation of dental periapical radiographs.BMC oral health · 2026Article
- AI-based diagnostic evaluation of GPT-4o for crown-fracture detection on maxillary periapical radiographs: effects of prompt detail and customization.BMC oral health · 2026Article
- Generating Findings for Jaw Cysts in Dental Panoramic Radiographs Using a GPT-Based VLM: A Preliminary Study on Building a Two-Stage Self-Correction Loop with a Structured Output (SLSO) Framework.Diagnostics (Basel, Switzerland) · 2026Article
- Generative Artificial Intelligence and Large Language Models in Paediatric Dentistry: A Scoping Review.International dental journal · 2026Article
- Evaluation of ChatGPT's ability to detect intraosseous pathologies with panoramic radiographs.BMC oral health · 2026Article
- Comparison of ChatGPT-4o and expert evaluation in endodontic education: a cross-sectional pilot study.BMC medical education · 2025Article
- Artificial intelligence versus human expertise: reliability of ChatGPT and the London atlas for dental age estimation using panoramic radiographs.BMC oral health · 2025Article
- Impaction Predictors and Diagnostic Performance of CBCT Versus Panoramic Radiography for Supernumerary Teeth in a Romanian Multicenter Cohort.Diagnostics (Basel, Switzerland) · 2025Article
- Performance of a Vision-Language Model in Detecting Common Dental Conditions on Panoramic Radiographs Using Different Tooth Numbering Systems.Diagnostics (Basel, Switzerland) · 2025Article
- Benchmarking large-language-model vision capabilities in oral and maxillofacial anatomy: A cross-sectional study.PloS one · 2025Article
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3 authors.
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Abstract
backgroundWith the growing capabilities of language models like ChatGPT to process text and images, this study evaluated their accuracy in detecting supernumerary teeth on periapical radiographs. A customized GPT-4V model (CGPT-4V) was also developed to assess whether domain-specific training could improve diagnostic performance compared to standard GPT-4V and GPT-4o models.
methodsOne hundred eighty periapical radiographs (90 with and 90 without supernumerary teeth) were evaluated using GPT-4 V, GPT-4o, and a fine-tuned CGPT-4V model. Each image was assessed separately with the standardized prompt "Are there any supernumerary teeth in the radiograph above?" to avoid contextual bias. Three dental experts scored the responses using a three-point Likert scale for positive cases and a binary scale for negatives. Chi-square tests and ROC analysis were used to compare model performances (p < 0.05).
resultsAmong the three models, CGPT-4 V exhibited the highest accuracy, detecting supernumerary teeth correctly in 91% of cases, compared to 77% for GPT-4o and 63% for GPT-4V. The CGPT-4V model also demonstrated a significantly lower false positive rate (16%) than GPT-4V (42%). A statistically significant difference was found between CGPT-4V and GPT-4o (p < 0.001), while no significant difference was observed between GPT-4V and CGPT-4V or between GPT-4V and GPT-4o. Additionally, CGPT-4V successfully identified multiple supernumerary teeth in radiographs where present.
conclusionsThese findings highlight the diagnostic potential of customized GPT models in dental radiology. Future research should focus on multicenter validation, seamless clinical integration, and cost-effectiveness to support real-world implementation.
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