The advent of big data and advanced analytics is transforming the landscape of toxicology. By integrating vast datasets from various sources, toxicologists can uncover complex patterns and correlations that were previously undetectable. This data-driven approach enhances the ability to predict toxicological risks and supports evidence-based decision-making.
Moreover, the use of machine learning algorithms in toxicology is enabling the development of predictive models that can anticipate the effects of chemical exposures, identify biomarkers of toxicity, and improve the classification of hazardous substances.
