Artificial Intelligence in Oral Cancer Histopathology: Current Applications and Future Perspectives

Abstract Oral squamous cell carcinoma (OSCC) remains one of the most prevalent malignancies affecting the oral cavity and continues to contribute significantly to cancer-related mortality worldwide. Histopathological examination is considered the gold standard for diagnosis; however, subjective interpretation, increasing diagnostic workload, and inter-observer variability may influence diagnostic consistency. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have introduced innovative approaches for analyzing histopathological images with greater speed and precision. AI-assisted pathology systems can support disease detection, grading, prognostic assessment, and prediction of malignant transformation. The integration of digital pathology and AI has the potential to improve diagnostic accuracy while facilitating personalized treatment strategies. This review discusses the current applications, benefits, challenges, and future prospects of AI in oral cancer histopathology. Keywords: Artificial intelligence, Oral squamous cell carcinoma, Digital pathology, Deep learning, Histopathology, Machine learning