ROC AUC is a summary measure of how well a binary classifier, such as an AI model predicting trial success or failure, separates positive from negative cases across thresholds. Clinicians often see AUC values quoted without context; understanding that 0.5 is no better than chance and that small changes may or may not be clinically meaningful is essential for proper appraisal.