RENSSELAER POLYTECHNIC INSTITUTE

AI Model Improves Clinical Trial Approval Prediction by Quantifying Uncertainty

8 months ago

• A new AI model developed by Stanford and Rensselaer enhances the prediction accuracy of clinical trial approvals by quantifying uncertainty. • The model integrates selective classification with the Hierarchical Interaction Network (HINT) to identify and withhold predictions for low-confidence trials. • Experiments showed the AI model achieved significant improvements in PR-AUC for Phase I, II, and III trials compared to the base HINT model. • Researchers aim to refine the model for more accurate clinical trial simulations and explore new applications in drug development.