Machine learning and artificial intelligence (AI) are pivotal in managing and interpreting big data in toxicology. Supervised learning algorithms can predict toxicological endpoints based on historical data. Unsupervised learning techniques can uncover hidden patterns and relationships within the data. Deep learning models, particularly those involving neural networks, are increasingly used for image analysis in high-content screening.
