99 Artificial intelligence (AI) has the potential to transform the early detection of oral cancer, according to a recent systematic review. Researchers from the University Visvesvaraya College of Engineering and Manipal Academy of Higher Education conducted the study, which emphasises how AI techniques, particularly machine learning and deep learning, could significantly improve the accuracy of diagnosing oral cancer, easing the burden on medical professionals and enhancing patient outcomes. The findings were published in the journal Healthcare Analytics. The study, which reviewed over 70 articles on AI-driven oral cancer detection methods, outlines the promising results of various AI algorithms in analysing medical imaging data. Oral cancer, which ranks as the sixth most common type of cancer globally, is often diagnosed at an advanced stage due to the lack of early detection. AI’s ability to automate the detection process offers hope for addressing this critical challenge. The study revealed that many AI algorithms have demonstrated remarkable accuracy in identifying oral cancer, with some models achieving up to 100% accuracy. The support vector machine (SVM) and logistic regression were among the highest-performing models, while other techniques, including decision trees, random forests, and convolutional neural networks (CNN), also exhibited strong detection capabilities. These algorithms excel in analysing medical images and distinguishing between cancerous and non-cancerous cells, crucial for timely and effective treatment. Deep learning, a subset of AI, has emerged as a particularly powerful tool in image classification. By using complex neural networks, AI models can simulate human decision-making and learn from vast amounts of data. The review found that CNNs and their variants, which are widely used for image recognition tasks, were effective in detecting oral cancer with high accuracy, making them a popular choice among researchers. The flexibility and scalability of these models mean they can be applied to different types of imaging data, including histopathology slides and 3D scans. Oral cancer is often linked to risk factors such as tobacco and alcohol use, poor oral hygiene, and genetic predispositions. Detecting the disease early is crucial, as it significantly increases survival rates. However, traditional diagnostic methods, which often rely on visual inspections and biopsies, can be time-consuming and subject to human error. The integration of AI into diagnostic workflows can reduce these delays, providing real-time analysis and reducing the need for invasive procedures. The study noted that while AI is not intended to replace clinicians, it can serve as a valuable tool for aiding diagnosis and reducing the burden on healthcare systems, particularly in low-resource settings. For instance, machine learning models could be deployed in mobile applications, enabling wider access to screening in rural or underserved areas. This could be especially beneficial in countries like India, where oral cancer rates are among the highest in the world. Despite the promising findings, the study acknowledged several challenges in implementing AI for oral cancer detection. One significant hurdle is the need for large, high-quality datasets to train AI models. Small sample sizes in many of the reviewed studies can limit the generalizability of results. Additionally, integrating AI into clinical practice will require significant collaboration between healthcare providers, researchers, and policymakers to ensure that these technologies are used effectively and ethically. The review also raised concerns about the transparency of AI decision-making processes. Understanding how AI algorithms arrive at their conclusions—known as the “black box” problem—remains a key issue. Efforts to improve the interpretability of these models will be essential to building trust among clinicians and patients alike.