What are the challenges faced when implementing AI and ML in Epidemiology?
Despite the benefits, several challenges exist:
1. Data Quality: The accuracy of AI and ML models heavily relies on the quality of input data. Inconsistent or incomplete data can lead to erroneous predictions. 2. Ethical Concerns: The use of AI in health data analysis raises ethical issues related to privacy, consent, and the potential for biased outcomes. 3. Interpretability: AI models, especially deep learning algorithms, often function as "black boxes," making it difficult to understand how they arrive at certain predictions. 4. Resource Availability: Implementing AI and ML requires substantial computational resources and expertise, which might not be readily available in all settings.
