Artificial intelligence and deep learning applications in oral oncology

Oral squamous cell carcinoma (OSCC) is a major health concern due to delayed diagnosis. Deep learning (DL) offers automated detection and classification of oral lesions, including oral potentially malignant disorders (OPMDs). A structured narrative review was conducted by searching PubMed, Web of Science, Scopus, and Google Scholar for peer-reviewed studies published between January 2020 and October 2025. Fifteen eligible studies using deep learning on clinical or smartphone images were qualitatively synthesized. Sensitivity ranged 55–98%, specificity 80-96.5%, with AUCs above 0.86. CNN models reached 80–95% accuracy, and some hybrid models exceeded 95%. Segmentation and object detection models achieved Dice coefficients up to 0.95 and mean average precision values of 74–85%. DL shows promise for early, non-invasive oral cancer and OPMD diagnosis. Limitations include small, imbalanced datasets and limited external validation. Unlike broader AI reviews, this review specifically compares recent image-based DL architectures and discusses their implications for clinical translation. Future research should prioritize multicenter datasets, external validation, explainable AI, and prospective clinical evaluation.