Despite its potential, AI in epidemiology has limitations. One major challenge is the quality of data available for analysis. Incomplete or biased data can lead to inaccurate predictions and ineffective responses. Additionally, the implementation of AI requires significant investment in infrastructure and skilled personnel. Ensuring the interoperability of different AI systems and integrating them into existing public health frameworks is also a complex task.
