The threat of infectious disease persists into the twenty first century, leaving stark vulnerabilities in worldwide systems of tracking of population health, which are still largely in the reactive, segmented and overly slow-moving phase in responding to outbreaks. This research critically analyzes the way artificial intelligence (AI) and cloud computing technology will help transform disease surveillance methods and introduce a novel paradigm: preventing outbreaks rather than detecting them at the early stages of attack. The research establishes that predictive analytics that use AI-based models pretrained on a wide range of data types including electronic health records (EHRs), wearable sensor information, mobility data, and even environmental data can foresee outbreaks even before an established system becomes aware of them. Cloud and edge computing also increase scalability and responsiveness enabling decentralized, synchronized monitoring both on the ground and across boundaries. With geospatial intelligence, the optimal way to allocate resources is to recommend adaptable policies through reinforcement learning (RL) whereas spatial visualization of the process of disease spread allows such distribution. Applications and case analyses in outbreaks of COVID-19, Ebola, and Zika demonstrates how AI could enhance surveillance accuracy and response time. Nevertheless, the research also notes that there are major challenges, such as privacy, and data-quality variations, algorithm-bias, inadequate interoperability, limited infrastructure, or insufficient infrastructure, as predominantly faced by the low- and middle-income economies. This paper contends that technological innovation will not provide the proper means of preventing diseases effectively without fair data management, effective cybersecurity, and inclusive system development. Overall, integrating AI-based predictive value and responsible governance will decide whether AI health clouds can turn into powerful solutions to prevent future outbreaks or reinforce existing disparities related to global health monitoring. The paper concludes with the recommendation to institutionalize representative datasets and open governance structures to help build trust, deploy ethically, and make the world healthier.
Ogunsakin et al. (Wed,) studied this question.