Oral cancer is a significant global health concern, with high morbidity and mortality largely attributable to delayed detection, often at advanced stages, due to the limitations of current diagnostic approaches. Although traditional methods are useful such as visual-tactile examination, biopsy, and adjunctive diagnostic aids are often subjective, time-consuming, and dependent on clinician expertise. On the counterview, artificial intelligence (AI) offers a promising alternative by enabling rapid, objective and accurate detection of early-stage oral malignancies. This narrative review explores the growing role of AI particularly machine learning and deep learning algorithms in the early detection of oral cancer. The review highlights how AI surpasses conventional methods in pattern recognition, diagnostic consistency, and scalability, especially in low-resource settings. Challenges related to data quality, model validation, and clinical integration is also discussed. Overall, AI presents a transformative tool in oral oncology, with the potential to significantly reduce diagnostic delays, improve early detection, and ultimately enhance patient outcomes.
Dhingra et al. (Fri,) studied this question.
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