This study, conducted by Hussein et al., focuses on using AI to manage dengue fever to strengthen disease control, optimize healthcare responses, and reduce the overall disease burden. There is no quantitative data collected in this study. Rather, the authors use findings from previous research to outline how AI methods can be applied to dengue management. They focus on five main ways that AI can aid in improving the management of dengue fever. First, they focus on the use of AI to expedite and refine case identification through patient symptoms, medical imaging, and laboratory results, meaning that AI detects subtle signs that are often missed by clinicians. Then, they argue that AI can start to recognize patterns in epidemiological, environmental, and social data, and forecast potential outbreak hotspots to improve early warning systems and pinpoint high-risk areas to optimize the implementation of preventive measures. Next, clinicians can use AI to optimize resource allocation in healthcare institutions as AI uses predictive analytics applied to historical and real-time hospital data to forecast patient surges. Then, they discuss that researchers can use AI to analyze environmental data that helps in determining the suitability of locations for mosquito breeding and, therefore, help regulate the transmission of dengue fever. Finally, they discuss that AI can aid in promoting public awareness through sorting through massive amounts of data online; they can help promote the spread of information to gauge awareness, detect misinformation, and adapt health campaigns. Overall, AI can significantly aid in the prevention of the spread of dengue fever as it can track mosquito development and also help in noticing patterns to prevent the mass spread of dengue fever after transmission to humans.