CURA: Clinical Uncertainty Risk Alignment for Language Model-Based Risk Prediction

ArXi:2604.14651v1 Announce Type: new Clinical language models (LMs) are increasingly applied to clinical risk prediction from free-text notes, yet their uncertainty estimates often remain poorly calibrated and clinically unreliable. In this work, we propose Clinical Uncertainty Risk Alignment (CURA), a framework that aligns clinical LM-based risk estimates and uncertainty with both individual error likelihoods and cohort-level ambiguities.