Paper Details

Name: Devesh Kumar Yadav , Chandramani & Divyanjali Tiwari
Country: India

Year: 2026
Volume: Volume: 13, Issue: 1, (January-June)
Page Number: 43-47
DOI: https://doi.org/10.5281/zenodo.18812267

ABSTRACT
Oral cancer remains a significant global health burden, with high morbidity and mortality rates, particularly in developing nations where delayed diagnosis and limited access to specialized care are common. Early detection is critical for improving survival outcomes; however, conventional
diagnostic methods often rely heavily on clinical expertise and are subject to variability and late-stage identification. In recent years, Artificial Intelligence (AI) has emerged as a promising adjunct in oral oncology, offering innovative approaches for early detection, risk assessment, and preventive care. AI-driven technologies, particularly machine learning and deep learning algorithms, enable the analysis of clinical images, radiographic data, histopathological slides, and molecular biomarkers with enhanced accuracy and reproducibility. These systems have demonstrated potential in identifying potentially malignant disorders, supporting diagnostic decision-making, and facilitating large-scale screening programs. AI-based tools also contribute to preventive strategies through personalized risk prediction and patient education. Despite these advancements, several challenges hinder
widespread clinical implementation, including data heterogeneity, limited external validation, ethical concerns, and the need for regulatory oversight. Issues related to algorithm transparency and data privacy must also be addressed to ensure responsible adoption. This review provides a comprehensive overview of current AI applications in oral cancer diagnosis and prevention, discusses associated advantages and limitations, and explores future directions for integrating AI into routine oral healthcare. With
continued research and ethical implementation, AI has the potential to significantly enhance early detection and improve patient outcomes in oral oncology.

Keywords: Artificial Intelligence, Oral Cancer, Deep Learning,
Early Detection, Prevention, Digital Pathology