An Empirical Analysis of Calibration and Selective Prediction in Multimodal Clinical Condition Classification

ArXi:2603.02719v2 Announce Type: replace As artificial intelligence systems move toward clinical deployment, ensuring reliable prediction behavior is fundamental for safety-critical decision-making tasks. One proposed safeguard is selective prediction, where models can defer uncertain predictions to human experts for review. In this work, we empirically evaluate the reliability of uncertainty-based selective prediction in multilabel clinical condition classification using multimodal ICU data.