Oral cancer is a significant global health concern, particularly in countries like India, where tobacco and betel nut use are prevalent. Despite advances in therapy, the prognosis remains poor, largely due to late-stage diagnosis. Early detection is key to improving survival rates and reducing the burden of treatment. In recent years, Artificial Intelligence (AI) has emerged as a revolutionary tool in medical diagnostics, with promising applications in oral oncology. This article aims to explore the role of AI in the early detection of oral cancer, its current applications, diagnostic accuracy, limitations, and the future direction of its integration into routine oral healthcare. An extensive review of the current literature was conducted, focusing on AI techniques such as machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs), as well as their applications in analyzing intraoral images, radiographs, histopathology slides, and salivary biomarkers. Clinical trials, pilot studies, and technological assessments were reviewed to evaluate the performance of AI in detecting oral potentially malignant disorders (PMDs) and early-stage squamous cell carcinoma. AI-based tools have shown considerable promise in the accurate and non-invasive diagnosis of oral lesions. These systems offer enhanced sensitivity and specificity, reduce human error, and provide objective assessments, even in low-resource or remote settings. DL algorithms, particularly CNNs, have demonstrated excellent performance in image recognition tasks relevant to oral pathology. However, challenges such as data standardization, algorithmic bias, lack of clinical validation, and ethical concerns still hinder widespread adoption. AI has the potential to transform early detection strategies for oral cancer by supporting clinicians in making faster and more accurate diagnoses. With proper validation, integration into clinical workflows, and adherence to ethical guidelines, AI can serve as an invaluable adjunct in oral medicine, especially for mass screening and personalized diagnostics. Continued research, investment in digital infrastructure, and training of dental professionals are essential for realizing its full potential in the future of oral healthcare.
Shukla et al. (Wed,) studied this question.