Despite their advantages, automated tools in epidemiology face several challenges. Data quality and accuracy are critical concerns, as automated systems rely on the input data's integrity. Privacy and security issues also arise, given the sensitive nature of health data. Furthermore, there is a need for standardization and interoperability of data across different systems and platforms. Ensuring that these tools are accessible and usable in low-resource settings remains a significant challenge.
