Bayesian uncertainty estimation improves clinical decision making in medical AI agents

• URL: http://arxiv.org/abs/2607.20582v1
• Date: Wed, 22 Jul 2026 11:54:23 GMT
• Title: Bayesian uncertainty estimation improves clinical decision making in medical AI agents
• Abstract summary: We show that Monte Carlo dropout provides an uncertainty signal that tracks generalisation across training-set scales and flags confident yet error-prone predictions.<n>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.
• Score: 3.251669068529209
• License: http://creativecommons.org/licenses/by/4.0/
• 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 ($Δ$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.

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