Uncertainty-Aware Calibrated Clinical Text Classification with Large Language Models

תקציר מקורי באנגליתarXiv:2509.19375v2 Announce Type: replace-cross Abstract: Large language models are increasingly used for clinical text classification, where overconfident misclassifications can directly affect patient care. Existing black-box uncertainty methods attach a confidence score to a fixed LLM prediction using softmax probabilities, verbalised confidence, prompt agreement, or generation consistency. These signals are often poorly calibrated and offer no mechanism for combining model evidence with prior clinical belief. We instead formulate closed-set clinical classification as likelihood-free posterior inference over diagnostic hypotheses. A prompt-conditioned LLM is treated as a class-conditional stochastic simulator: for each candidate diagnosis it generates synthetic clinical descriptions, wh