Abstract
The diagnosis and treatment of oral cancer present significant challenges, including delayed diagnosis at advanced stages and limited access to healthcare. Deep learning (DL), a subset of artificial intelligence, holds promise for transforming medical image analysis and predictive analytics. In this perspective, we examine the applications of DL in oral cancer. Specifically, we explore the efficacy of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in diagnosing and predicting the prognosis of oral cancer in the last five years. Additionally, we underscore the importance of oral cancer databases in advancing research and clinical practice. However, DL methods face constraints related to input variability and model interpretability. Addressing these issues is crucial to harnessing the full potential of DL in oral cancer treatment. In summary, this article underscores the innovative contributions of DL in revolutionizing oral cancer management and advocating for precision medicine in oncology.
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