Early detection in oral cancer: are we ready for AI-driven precision?

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 illnesses. However, traditional diagnostic approaches are typically laborious, subjective, and unreliable, leading to delayed diagnosis - when therapy options are limited and survival is compromised. In oral cancer, artificial intelligence (AI) and precision medicine are becoming game-changing technologies that enhance individualized care, treatment planning, and diagnostic precision. Machine learning and deep learning algorithms, particularly convolutional neural networks, can analyze massive, complex datasets from fluorescence to hyperspectral imaging, revealing patterns that are beyond human detection. Recent trials have shown 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 and individualized oral cancer therapy relies on creating Explainable AI and strong ethical frameworks that encourage transparency, trust, and equitable integration.