Artificial intelligence is undoubtedly emerging, and its various manifestations in technology are widely and deeply embedded in our communities. That is what obliges its mindful and skillful use and utilization, especially for infectious disease prevention. Over 1 million people are affected by infectious diseases, and the whole globe is carrying a huge burden of DALYs due to infection mortality and morbidity, which can be mitigated by the proper use of machine learning and deep learning features for data analytics and monitoring of real-time changes, and even point out the anticipated timing of pandemics. The application of machine learning and deep learning allows for forecasting and monitoring of outbreaks, which can contribute to converting the distribution of medical resources into an efficient, patient-centered approach. There are various algorithms and ML models applied in infectious disease surveillance to promote public health security. The following review considers the interface between AI and public health, with considerations of successful applications and concerns in technology acceptance and governance. Key public health policy recommendations derived from recent literature are also presented.
Borham et al. (2025) studied this question.