What Are the Challenges of Using Machine Learning in Toxicology?
Despite its potential, the application of ML in toxicology is not without challenges:
Data Quality: The accuracy of ML models heavily depends on the quality and completeness of the training data. In toxicology, data can be sparse or heterogeneous, complicating model development. Interpretability: ML models, especially deep learning ones, can be complex and difficult to interpret, which poses a challenge for regulatory acceptance and scientific validation. Overfitting: There is a risk of overfitting, where the model performs well on training data but poorly on unseen data, limiting its real-world applicability.
