Abstract :
Artificial intelligence (AI) is poised to revolutionize oral pathology, with oral cancer diagnosis emerging as its major frontier, offering faster, more accurate, and accessible solutions that overcome the limitations of conventional methods. However, conventional diagnostic approaches such as histopathology and cytology are often limited by delayed turnaround times, which may hinder their early detection and treatment. In cytology and community-based screening, AI tools—including smartphone-based applications—are bridging gaps in low-resource settings by enabling scalable early detection. Digital histopathology involving AI-powered whole-slide image (WSI) analysis enables automated detection of OSCC, dysplasia, and precancerous lesions with improved reproducibility. Furthermore, prognostic and predictive models leveraging genomic and proteomic data are advancing personalized care, while adjunctive applications in radiology and salivary biomarker analysis expand AI’s diagnostic scope. The integration of AI with precision medicine, explainable AI (XAI), AI-assisted education, and hybrid human–AI diagnostic models holds promise for shaping the next diagnostic frontier in oral pathology.Despite these advances, barriers such as dataset biases, the lack of standardized digital pathology infrastructure, ethical issues related to data confidentiality and algorithm transparency, and the risk of over-reliance on AI remain critical limitations. Addressing these challenges is crucial for the safe and equitable adoption of clinical practices.This article examines the role of AI development in oral pathology, highlighting its applications, benefits, problems, and future directions, and its role as a synergistic tool. This review assesses the application, diagnostic accuracy, efficiency and accessibility of AI, machine learning (ML) and Deep learning (DL) in oral pathology landscape.
Keyword :
Artificial Intelligence (AI), Oral Pathology, Diagnostics, Machine Learning, Deep Learning, OSCC, Health Care