For the majority of cancers, early detection results in better survival. Oral cancer is one of the few cancers that is visible and many of these cancers are preceded by a potentially malignant lesion where medical intervention can prevent the development of cancer. Taken together, oral cancer presents an opportunity for early detection. However, identifying which oral lesion has a propensity to become oral cancer is not straightforward without specialised training and this problem is confounded by the lack of specialists who are trained in this expertise particularly in low- and middle-income countries, where the majority of oral cancers are diagnosed. One innovative approach to overcome this is to develop an artificial intelligence algorithm to classify oral lesions into those that are benign and those that are potentially malignant or are occult cancer so that patients can be triaged accordingly to receive appropriate clinical management. In this project, we propose to work within a multi-disciplinary, international team to collate a library of images from existing and prospective collections that will facilitate the development of an artificial intelligence algorithm that will be tested and validated. The outcome of this project will pave the way for further rigorous testing, development of an App incorporating this automated tool and clinical validation for the early detection of oral cancer. The development of an automated tool for the classification of oral lesions will facilitate the identification of patients most at risk to develop oral cancer so that these individuals can be managed appropriately. This project is particularly impactful in the low- and middle-income countries as the majority of the global burden of oral cancer is found in these countries.