Abstract
BACKGROUND: Early diagnosis of oral squamous cell carcinoma (OSCC) remains challenging, with survival largely stage-dependent at presentation. Artificial intelligence (AI) promises to enhance detection and clinical decision-making across clinical photographs, radiology, optical imaging, and digital pathology. METHODS: This narrative review synthesizes peer-reviewed PubMed-indexed English-language studies up to October 2025, prioritizing prospective designs, external validation, and clinically interpretable models. We focus on tasks relevant to clinicians: lesion triage from clinical images, prediction of nodal metastasis on CT/MRI/PET, margin assessment with optical modalities, and histopathology-based diagnosis/grading. We also discuss implementation issues: dataset shift, bias, and reporting standards. RESULTS: In clinical photographs, deep learning achieves high diagnostic accuracy for OSCC and oral potentially malignant disorders (OPMD) classification in single-center studies and shows promising generalization with multi-site external testing, yet performance still degrades on out-of-distribution images and under real-world artifacts. In radiology, radiomics and deep learning models improve risk stratification and prediction of cervical nodal metastasis beyond conventional imaging, particularly with multimodal feature fusion. Optical methods such as hyperspectral spatial frequency domain imaging and OCT combined with AI show feasibility for intraoperative margin assessment and in-clinic triage. Digital pathology models on whole-slide images approach expert-level classification for OSCC diagnosis and are beginning to predict malignant transformation risk in oral epithelial dysplasia; however, rigorous prospective validation remains scarce. CONCLUSION: AI systems for OSCC are maturing and clinically oriented. Before routine adoption, studies must demonstrate external validity, clinician-in-the-loop performance, calibration, and impact on time-to-diagnosis and patient outcomes. Pragmatic trials and transparent reporting are essential to move beyond proof-of-concept into equitable clinical benefit.