Biomarker-based machine learning for malignant transformation in oral potentially malignant disorders: a scoping review

Oral squamous cell carcinoma (OSCC) is often preceded by oral potentially malignant disorders (OPMDs). Despite this known association, the transition from an OPMD to OSCC is complex, unpredictable, and non-linear, making early detection and intervention challenging for clinicians. Histopathological grading, the current standard for risk stratification, is not reliably predictive of malignant transformation (MT), and is subject to significant inter- and intra-observer variability. This scoping review evaluates emerging evidence on the integration of artificial intelligence (AI) and machine learning (ML) with molecular and histopathologic biomarkers to enable individualized risk assessment for MT. Ten retrospective studies incorporating AI/ML algorithms were analyzed, utilizing biomarkers ranging from gene expression panels, biochemical and protein-based markers like S100A7 to image-derived histomorphometric features. These models demonstrated promising predictive accuracy, with histology-derived features showing the greatest clinical feasibility. However, variability in methodologies, lack of prospective validation, and inconsistent demographic reporting limit the generalizability of the findings. This review highlights the need for multimodal biomarker integration, prospective clinical trials, and validation across broader populations. Ultimately, AI/ML-enhanced tools hold significant potential to inform personalized surveillance and treatment decisions in OPMD, but their clinical readiness requires further refinement and robust validation.