Image-Based Artificial Intelligence for Predicting Malignant Transformation of Oral Potentially Malignant Disorders:...

Background: Oral potentially malignant disorders (OPMDs) have a complex, but not consistently consistent, risk of transformation to oral squamous cell carcinoma (OSCC). The histopathologic grading of dysplasia is the traditional means of prognosis although there is considerable inter-observer variability and the tool has poor predictive value. Thus, image-based AI-based methods such as computational pathomics (CP), clinical-photograph deep learning (CDL), and optical or spectroscopic image analysis via machine learning (ML) or deep learning (DL) have been proposed as potential non-invasive risk stratification methods. Aim of the review is to identify the current state of the evidence for AI applications in the field of image-based diagnosis and treatment of OPMDs, their methodological characteristics and predictive accuracy, and priorities for future research. Methods: the scoping review was conducted using Joanna Briggs Institute methodology and PRISMA-ScR guidelines. The PubMed/MEDLINE, Scopus, Web of Science and Embase databases were searched between January 2018 and March 2026. Inclusion criteria were studies that used quantitative image analysis, ML or DL to diagnose, prognosticate, or stratify risk of OPMD in OPMD cohorts. A pre-piloted form was used to extract data which were then synthesized descriptively. Results: the 423 records identified included 24 (16 primary image-based studies and 8 contextual reviews) that met the inclusion criteria. The majority of studies were retrospective (15/16), and computational pathomics (n = 10), clinical-photograph deep learning and optical/spectroscopic imaging (n = 4) were emphasized. There was no study that used the conventional radiologic radiomics (CT, MRI, CBCT, or PET/CT) in OPMD cohorts. Overall, predictive performance was good, with AUROC values ranging from 0.73 to 0.96 for the detection of malignant transformation, 0.94 to 0.97 for OPMD versus OSCC discrimination, and 0.90 to 0.97 for dysplasia grading. Only 44% contained external validation, only 6% were prospective, and only limited adherence was made to IBSI, TRIPOD-AI and CLAIM standards. Conclusions: AI-based image recognition for OPMDs shows good predictive performance, and it has yet to be developed to a mature stage of the process. Future multicentre study, image standardisation, external validation and better reporting standards are needed. Remarkably, radiologic radiomics is an important and unexplored research gap in OPMD risk prediction.