It is shown that Monte Carlo dropout, applied to a multi-task chest-radiograph classifier, provides an epistemic uncertainty signal that tracks generalisation across training-set scales and flags confident yet error-prone predictions.
Abstract
Machine learning models for medical image analysis typically lack a reliable measure of confidence, limiting their use in ambiguous or atypical cases. Here we show that Monte Carlo dropout, applied to a multi-task chest-radiograph classifier (eight thoracic findings, 137,593 training images), provides an epistemic uncertainty signal that tracks generalisation across training-set scales and flags confident yet error-prone predictions. Adding this signal to the point prediction raised error-detection AUROC from 0.74 to 0.77 ($\Delta$AUROC +0.023, 95% CI [+0.014, +0.033]). In a controlled 2x2 factorial experiment, a clinical-decision-support agent exploited this uncertainty only when it was delivered as a binary error-risk flag rather than as raw scores, cutting confident misdiagnoses on unreliable findings from 8.5% to 2.7%. Epistemic uncertainty estimation thus carries decision-relevant information beyond point predictions, but its value for downstream agents depends on how it is communicated.
A rigorous empirical framework is presented for comparing three uncertainty quantification approaches on two clinical prediction tasks, in-hospital mortality and 30-day readmission, using 74,829 ICU admissions from the MIMIC-IV database to support a more demanding evaluation standard for UQ in clinical machine learning...
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The Expected Gradients Reconstruction Uncertainty Estimate (egRUE), which incorporates prediction explanations into its uncertainty computation and decomposes uncertainty into feature-wise contributions, and proves theoretical properties of egRUE and shows that it improves reliability and interpretability compared to e...
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Although UQ has the potential to improve the safety and interpretability of AI-assisted screening, challenges remain, including calibration, threshold selection, computational cost, and the need for prospective clinical validation.
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It is suggested that case-level consistency provides a meaningful practical signal for reliability estimation in medical imaging, whose relationship to the classical epistemic–aleatoric decomposition warrants further theoretical investigation across broader clinical settings.
Muhannad Faleh Alanazi, B. Z. Shakhreet, Hattan Ali A. Asiri et al.· Scientific Reports· 0 citations
An uncertainty-aware machine learning framework that integrates probabilistic modeling techniques into conventional predictive architectures to jointly estimate epistemic and aleatoric uncertainty is proposed, indicating that incorporating uncertainty estimation enhances trustworthiness and robustness without sacrifici...
Evi Yulianingsih, E. Noche, V. Yadav et al.· Journal of Data Science· 0 citations
A knowledge-based expert system using a Bayesian Network to explicitly model an expert neurosurgeon's clinical reasoning for predicting outcomes of Gamma Knife radiosurgery highlights a limitation of predictive paradigms based on current expert knowledge and suggests the need for iterative refinement of knowledge-based...
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