AI for Classifying Oral Cancer and Precursor Lesions Using Visible-Light Photography

Abstract Artificial intelligence shows promise for oral cancer detection, yet clinical translation remains limited. This scoping review examined 134 studies (2015–2025) investigating AI applications for oral lesion classification using visible-light clinical photography. Searches across Scopus, Web of Science, Embase, and PubMed followed PRISMA-ScR guidelines. Methodological limitations exist among studies; 25.4% utilised a single 131-image Kaggle dataset without ground-truth histological labelling, 99.3% employed supervised learning, and 8.2% performed external validation. Binary classification tasks predominated (59.7%), while dysplasia grading was seldom explored (10.4%). Convolutional neural network architectures, such as ResNet, dominated study designs. Critical gaps include limited multi-modal and multi-model integration, absence of ordinal classification approaches - reflecting disease progression, and underexplored potential of novel deep-learning architectures such as graph-based mechanisms, and use of frontier techniques to address data scarcity such as synthetic image generation.