What are the Challenges of Implementing AI and ML in Epidemiology?
Despite their potential, integrating AI and ML into epidemiology comes with challenges:
Data Quality: The accuracy of AI models depends on high-quality, representative data. Privacy Concerns: Handling sensitive health data requires stringent privacy protections. Interpretability: Complex algorithms can be difficult to interpret, limiting their practical application. Bias: AI models can be biased if the training data is not representative of the population.
