From Static Images to Living Models: Digital Twin‐Driven AI for Precision Risk Prediction in Oral Potentially Malignant Disorders

Oral potentially malignant disorders (OPMDs) are an important clinical problem in the field of oral medicine owing to their heterogenous biological behavior and unpredictable risk of malignant transformation. A significant percentage of oral squamous cell carcinoma (OSCC) cases occur from lesions such as oral leukoplakia, erythroplakia, oral lichen planus, and oral submucous fibrosis across the globe. Recent advances in surgical management and oncologic therapy for OSCC have been little effective to improve patient's overall survival, mainly due to the fact that a group of patients are often diagnosed at advanced stages. Thus, effective risk stratification and early identification of OPMDs remains an essential focus in oral cancer prevention (Warnakulasuriya et al. 2021). Current diagnostic approaches are chiefly based on clinical examination and subsequent histopathological analysis of biopsy samples. While histopathology is still the gold standard for diagnosis, it has several known limitations that are widely acknowledged such as sampling bias, inter-observer variability when assessing grade of dysplasia and relatively limited predictive capacity in predicting long-term risk of malignant transformation. These limitations underscore the necessity for new technologies that can provide a more objective and prognostic assessment of oral mucosal lesions (Figure 1). Neural networks, especially in the form of convolutional neural networks (CNNs), have recently shown promising diagnostic performance when it comes to automated detection of oral lesions from clinical photographs and imaging datasets. Deep learning (DL) algorithms have shown high sensitivity and specificity, with several studies reporting success in identifying suspicious oral mucosal lesions. Most existing artificial intelligence (AI) models, however, operate as static classifiers applied to single images without integrated patient-specific biological information or longitudinal disease progression. Digital twin (DT) technology could provide a robust framework to alleviate these issues. A DT is a computational model of an individual patient, which leverages integrated data from diverse sources including clinical imaging, histopathology, molecular biomarkers, demographic characteristics, and longitudinal clinical observations to create a dynamic virtual representation of the disease behavior. In this way, CNN-based image processing could act as the first layer of such a platform and extract diagnostic features from images of oral lesions, while the DT updates predictions for disease progression and risk of malignant transformation (Yadalam, Arumuganainar, et al. 2025; Udayakumar et al. 2026). The oral cavity provides a unique opportunity for DT modeling due to its accessibility for repeated imaging and clinical monitoring. This targeted approach has shown promise, and it can be further enhanced by utilizing multimodal data sources including optical imaging techniques, histopathology findings, salivary biomarkers, and clinical photography to create prospective DT frameworks that generate individual risk profiles and monitor OPMD evolution over time. These approaches might facilitate targeted surveillance strategies, direct focused biopsies, and even help clinicians to detect 'high-risk' lesions that require timely intervention. Despite these encouraging prospects, however, some challenges remain to be solved before routine clinical implementation of DI-enabled diagnostic systems. This includes the need for standardized imaging protocols, large annotated datasets to train models on, multicentric validation studies, and more explainable AI methods that enable clinical interpretability. Attention must also be paid to ethical issues surrounding data privacy, algorithmic bias, and regulatory approval of AI-based medical devices (Kamel Boulos and Zhang 2021). Finally, this combined use of DT technology and DL-based image analysis is an emerging paradigm in precision oral medicine. Integrating multimodal patient data with dynamic computational modeling, digital twins may allow for more precise prediction of the progression towards malignant transformation in OPMDs and enable tailored surveillance approaches (Yadalam, Krithikadatta, et al. 2025). This notion will require upcoming multidisciplinary work between oral biology experts, data scientists, and biomedical engineers to be realized into technologies that can be clinically used. Saravanan Sampoornam Pape Reddy: investigation, writing – review and editing, validation, visualization, project administration, resources, data curation. Delfin Lovelina Francis: conceptualization, investigation, writing – original draft, methodology, software, formal analysis, validation, project administration. The authors declare no conflicts of interest. The authors have nothing to report.