Improving predictive accuracy in toxicology requires a multifaceted approach. Enhancing the quality and quantity of data available for model development is crucial. This involves integrating data from diverse sources, including human, animal, and environmental studies. Advancements in computational techniques, such as machine learning and deep learning, can also contribute to better model development and validation. Furthermore, fostering collaboration among researchers, regulatory agencies, and industries can lead to the standardization of methods and the sharing of best practices.
