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Artificial Intelligence-Based Evaluation of Oral Potentially Malignant Disorders Using Photographic and Clinical Data: A Retrospective Exploratory Diagnostic Accuracy Study.

Created on 26 Oct 2026

Authors

Domenico De Falco, Fabiana Pinto, Alberta Lucchese, Massimo Petruzzi

Published in

Dentistry journal. Volume 14. Issue 10. Oct 10, 2026. Epub Oct 10, 2026.

Abstract

Objective: The objective of this study was to evaluate the diagnostic performance of GPT-5.2 Thinking in identifying oral potentially malignant disorders within a patient sample that also included oral carcinomas and other oral cavity lesions, using intraoral photographs and associated clinical data. Methods: This retrospective exploratory diagnostic accuracy study included 103 oral lesions: 69 oral potentially malignant disorders, 25 oral carcinomas, and 9 other oral pathologies. Each case was assessed in three independent sessions, and the majority diagnosis was compared with the final clinical and/or histopathological reference diagnosis. Results: For oral potentially malignant disorders, sensitivity was 92.75%, specificity was 88.24%, accuracy was 91.26%, positive predictive value was 94.12%, negative predictive value was 85.71%, and Cohen's kappa was 0.804. Sensitivity was 81.82% for homogeneous leukoplakia, 78.57% for non-homogeneous leukoplakia, and 86.36% for oral lichen planus. For oral carcinomas, sensitivity was 84.00%, specificity was 97.44%, accuracy was 94.17%, positive predictive value was 91.30%, negative predictive value was 95.00%, and Cohen's kappa was 0.837. Conclusions: GPT-5.2 Thinking showed promising diagnostic performance in this exploratory case-enriched dataset, but predictive values require validation in representative clinical populations.

PMID:
42930423
Bibliographic data and abstract were imported from PubMed on 26 Oct 2026.

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