Uveal Melanoma (UM), a deadly cancer arising in the eye, is the most common eye cancer in adults. Early detection of UM is important due to the cancer’s ability to spread to the rest of the body early and because effective treatments are available to reduce its spread. Despite the availability of effective treatments, more than half of patients’ cancer spreads to the rest of the body, suggesting that UM may spread before the time of treatment. Choroidal nevi are benign tumors that are commonly seen in patients’ eyes and rarely can turn into cancer. Choroidal nevi can look like UM, which makes the diagnosis of these eye tumors challenging. Therefore, there is a need to identify and treat small UM to minimize the number of tumors that are observed and subsequently grow during the observation period. However, current screening methods face inherent limitations, particularly in regions with limited access to specialized eye cancer doctors. Machine learning (ML) is a field of study in artificial intelligence that can be used to assist in disease diagnosis. ML offers a promising approach to improve the identification and evaluation of eye tumors, thereby providing a potential tool for eye doctors both in the community and who specialize in eye cancer. Despite the significant research being done with ML in medical imaging, few studies have worked towards an ML tool for eye tumors like choroidal nevi and UM. Our previous work used ML to screen images of patients’ eyes to successfully find choroidal nevi and UM. We also used ML to evaluate images and ultrasound of patients’ eyes to assess choroidal nevi and UM for their ability to spread to the rest of the body. The objective of this proposed project is to assess the ability of ML to diagnose choroidal nevi and UM. We will use a large collection of images from the University of Illinois Chicago, which includes multiple different types of images taken of many patients with choroidal nevi and UM. We propose to develop and then improve ML tools for the screening and diagnosis of choroidal nevi and UM. We will use additional information about the patient’s medical history to help the tools perform better. We will also annotate the images before providing them to the ML tools to see if this improves the performance in screening and diagnosing choroidal nevi and UM. After identifying the best-performing ML tool in this research study, we will test this tool to perform other tasks related to the choroidal nevi and UM. We will test the ML tool’s performance in predicting the likelihood that a choroidal nevus will grow and the likelihood that a UM will spread to the rest of the body. We expect these results to provide a better understanding of how ML can be developed into a clinically useful tool to inform and guide management decisions for choroidal nevi and UM in community eye clinics and eye cancer specialist clinics to potentially save patient lives.