AI-Assisted Framework for Early Oral Cancer Screening Using Smartphone Imaging: A Review

Abstract

Oral cancer is one of the most prevalent forms of cancer worldwide, particularly in developing countries where tobacco use, alcohol consumption, and poor oral hygiene are common risk factors. Early diagnosis significantly improves treatment outcomes and survival rates; however, limited access to healthcare facilities and specialized diagnostic services often leads to delayed detection. Recent advances in artificial intelligence (AI), deep learning, and smartphone technology have created new opportunities for accessible and cost-effective screening solutions. This review paper examines the role of AI-assisted smartphone imaging systems in early oral cancer screening. It discusses image acquisition techniques, preprocessing methods, deep learning-based classification approaches, publicly available datasets, performance evaluation metrics, and challenges associated with smartphone-based diagnosis. The review highlights the potential of convolutional neural networks (CNNs) and mobile health technologies in improving early detection and supporting healthcare professionals, particularly in rural and underserved regions.