Introduction: Oral cancer is a deadly disease that affects millions of people worldwide. Early detection of oral cancer is crucial for effective treatment and increased survival rates. The detection of cancer is a complex process that demands a comprehensive array of tools and a high degree of expertise. Therefore, it is pertinent to underscore the utilization of artificial intelligence (AI) in this sphere, as it holds great potential for enhancing accuracy and efficiency in cancer diagnosis.
Aim: To evaluate the use of artificial intelligence with whole slide imaging in the detection of oral cancer.
Method: A comprehensive search of online databases including PubMed and Google Scholar was conducted to identify studies that evaluated the use of AI in oral cancer detection. The search keywords included "artificial intelligence," "oral cancer," "diagnosis," ,"detection", “whole slide imaging”. A total of 10 articles fulfilled the inclusion and exclusion criteria and were included. The pertinent data was extracted and recorded.
Result: The studies included in this review used different AI techniques, including machine learning (ML) algorithms, deep learning (DL), and neural networks (NN) in detecting oral cancer. The accuracy of AI-based methods was compared with conventional diagnostic methods, such as biopsy and histopathology. AI-based methods showed higher sensitivity, specificity, and accuracy compared to traditional diagnostic methods.
Conclusion: AI-based methods can complement traditional diagnostic methods and provide a faster and more cost-effective diagnosis, leading to improved patient outcomes.