This study, presented by Abdi and colleagues, examines how artificial intelligence (AI) can enhance surveillance, prediction, and control of vector-borne diseases in a changing global health landscape. To investigate the applications of AI in public health programs, image recognition systems, machine learning forecasting models, and optimization tools were evaluated through their efficacy in processing community generated data. Additionally, ethical considerations were considered for the implementation of AI in public health tools, such as the need for patient privacy. It was found that convolutional neural networks (CNN) are particularly effective at identifying mosquito vectors with over 90% accuracy. This allows expedited and targeted response to disease outbreaks. Further, machine models which incorporate variables such as climate and satellite images improve outbreak forecasting. Resource optimization systems were found to reduce insecticide use by 20-40%, improving resource distribution. The study demonstrates the many beneficial uses of AI in public health; however, these benefits are underscored by several limitations. The application of these uses is limited in resource-low settings, where they would prove the most beneficial. Additionally, the tools require significant community involvement, which limits the efficacy of said tools if the community does not participate sufficiently.