Early detection in oral cancer: Are we prepared for artificial intelligence-driven precision medicine?

Abstract

Oral cancer, particularly oral squamous cell carcinoma, remains a serious health concern, with a poor prognosis and a late diagnosis. Leukoplakia, erythroplakia, lichen planus, and submucous fibrosis are examples of oral potentially malignant disorders that must be detected early but are not always so by traditional, laborious, and subjective diagnostic techniques. In oral cancer, artificial intelligence (AI) and precision medicine are becoming game-changing technologies that enhance individualized care, treatment planning, and diagnostic precision. Complex imaging and histopathology data may be analyzed using machine learning and deep learning algorithms, particularly convolutional neural networks, which can identify patterns that are invisible to the human eye. AI systems based on smartphones have demonstrated expert-level accuracy in identifying oral lesions in recent experiments. Through the discovery of biomarkers and the integration of several omics, AI-driven precision medicine also makes customized treatments possible. Nonetheless, issues with patient privacy, data bias, and the opaque “black box” nature of AI systems persist. The future of proactive, individualized oral cancer care will be shaped by the development of Explainable AI and robust ethical frameworks, both of which are necessary to ensure transparency, trust, and equitable integration.