An Empirical Analysis of Calibration and Selective Prediction in Multimodal Clinical Condition Classification
This work addresses a critical problem for clinicians and AI developers, highlighting the need for calibration-aware evaluation to ensure safety and robustness in clinical AI, particularly in high-stakes clinical deployment scenarios.
The authors tackled the problem of selective prediction in multimodal clinical condition classification and found that it can substantially degrade performance despite strong standard evaluation metrics, with severe class-dependent miscalibration occurring in models. Their results show that selective prediction can assign high uncertainty to correct predictions and low uncertainty to incorrect ones, particularly for underrepresented clinical conditions.
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. Across a range of state-of-the-art unimodal and multimodal models, we find that selective prediction can substantially degrade performance despite strong standard evaluation metrics. This failure is driven by severe class-dependent miscalibration, whereby models assign high uncertainty to correct predictions and low uncertainty to incorrect ones, particularly for underrepresented clinical conditions. Our results show that commonly used aggregate metrics can obscure these effects, limiting their ability to assess selective prediction behavior in this setting. Taken together, our findings characterize a task-specific failure mode of selective prediction in multimodal clinical condition classification and highlight the need for calibration-aware evaluation to provide strong guarantees of safety and robustness in clinical AI.