Evaluation of Artificial Intelligence in Diagnosis and Risk Assessment of Oral Potentially Malignant Disorders

Oral potentially malignant disorders (OPMDs) are mucosal lesions that carry a risk of malignant transformation into oral cancer. Unfortunately, a general lack of knowledge and awareness of OPMDs is common among general dental practitioners. While thorough clinical examinations coupled with biopsy can identify most OPMDs, the absence of reliable non-invasive diagnostic tools and standardized risk stratification often delays early diagnosis and treatment of oral squamous cell carcinoma (OSCC).Early detection of suspicious oral lesions is crucial for reducing OSCC-related mortality and improving patient outcomes. Histopathological assessment of biopsied tissue remains the gold standard for diagnosis. However, since biopsy is invasive and may be associated with patient discomfort; numerous noninvasive diagnostic technologies have emerged to enhance the detection and diagnosis of oral mucosal lesions.Toluidine blue (TB) staining is one such adjunctive tool, where the degree of color retention aids in lesion characterization. Dark blue staining is considered positive for lesions highly suspicious for malignancy; light blue retention is considered positive for premalignant lesions pending histopathological confirmation, while lesions showing no stain retention are classified as negative.Exfoliative cytology represents another non-invasive diagnostic approach, wherein cells obtained via brushing the oral mucosa are spread on a slide for cytological evaluation. This technique, widely accepted and increasingly utilized, has proven valuable for early cancer detection. Notably, confocal microscopy has demonstrated high sensitivity and specificity (93%) in detecting malignant cells in exfoliative cytology specimens. Currently, TB staining and confocal microscopy remain the most commonly utilized non-invasive screening techniques in clinical practice.In recent years, artificial intelligence (AI) applications have shown remarkable promise in oncology, achieving high diagnostic accuracy across various cancer types. Deep learning models, in particular, offer exceptional performance, suggesting that AI-based solutions may be feasible for widespread community screening programs following further validation. In many cases, AI models have produced diagnostic outcomes that match or surpass those of experienced pathologists. Moreover, the combined application of AI with expert human evaluation has been shown to reduce diagnostic errors and improve diagnostic precision, particularly for poorly differentiated tumors and rare cases.Several studies have been done using different AI Models and revealed a promising application of AI in diagnosing OPMDs and cancers in different body sites.