What Challenges Exist in Implementing AI in Toxicology?
Despite the advancements, several challenges exist in implementing AI in toxicology:
Data Quality: High-quality and standardized data are essential for training reliable AI models. Inconsistent or incomplete data can lead to inaccurate predictions. Model Interpretability: AI models, especially deep learning models, can be complex and difficult to interpret, making it challenging to understand the underlying mechanisms of toxicity. Regulatory Acceptance: Regulatory agencies may be hesitant to adopt AI-based methods without robust validation and standardization. Integration with Existing Systems: Integrating AI tools with existing toxicological frameworks and databases can be complex and resource-intensive.
