We live in a time when investigators have overwhelming amounts of health-related data at their fingertips. In this podcast, Nicole Kleinstreuer explains how environmental health scientists are using machine learning to make sense of the information in those data—for example, predicting toxicological end points based on large curated data sets. But even as machine learning advances, researchers are working to set realistic expectations and performance thresholds for these new methods. Visit the podcast webpage to download the full transcript of this podcast.