Exploring the Potential of Artificial Intelligence for Diagnosing Oral Cancer: A Review of Imaging and Computational Techniques

Abstract

Oral cancer (OC) is associated with poor survival rates and remains one of the most lethal malignancies worldwide, ranking among the top high-risk tumors. Early and correct diagnosis is critical for improving survival; however, traditional diagnostic techniques like as clinical examination and biopsy are frequently time-consuming and intrusive. Recent breakthroughs in artificial intelligence (AI) provide exciting prospects to address these restrictions by allowing the analysis of large medical datasets such as imaging records, molecular data, and clinical parameters. The incorporation of artificial intelligence into oral cancer diagnostics brings novel approaches to early diagnosis, prognosis prediction, and therapy planning. Machine learning and deep learning algorithms can quickly and reliably evaluate medical imaging data, supporting physicians in detecting cancerous changes that would otherwise go undiscovered. Oral cancer lesions can be automatically detected and classified using AI-driven systems in conjunction with optical imaging methods or intraoral photography data. AI enhances patient outcomes by facilitating prompt clinical interventions and individualized therapeutic decision-making, in addition to increasing diagnostic accuracy. With an emphasis on automated image interpretation, computer-aided diagnostic tools, and machine learning-based models used for oral cancer detection, classification, and clinical management, this study emphasizes the current uses of artificial intelligence in oral cancer diagnosis.