<![CDATA[Oral cancer remains a global health challenge, with late-stage diagnosis contributing to high morbidity and mortality rates. Early detection is critical for improving survival outcomes, but traditional diagnostic methods often rely on subjective interpretation and are time-consuming. This study investigates the application of artificial intelligence (AI) in the early detection of oral cancer, emphasizing its potential to improve diagnostic accuracy and efficiency. A structured literature review examined studies published between 2015 and 2025 from databases such as PubMed, Scopus, and Web of Science. The review focused on research utilizing machine learning algorithms, particularly convolutional neural networks (CNNs), to analyze clinical and imaging data. Key performance indicators, including sensitivity, specificity, and accuracy, were evaluated to assess the effectiveness of AI technologies. The findings suggest that AI-driven diagnostic systems consistently surpass traditional methods, offering enhanced accuracy and reliability. These advancements highlight AI's transformative potential in streamlining diagnostic workflows and addressing challenges in resource-limited settings. In conclusion, AI technology is reshaping the early detection landscape for oral cancer, reducing delays, and improving diagnostic precision. However, further research is needed to address challenges such as data standardization, model transparency, and ethical considerations to facilitate broader clinical adoption.]]>
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