Predictive Entropy as a Joint Screen for Error and Paraphrase Instability in Medical Vision-Language Models
Medical Vision-Language Models (VLMs) answering binary presence questions on chest radiographs can fail in two linked ways: they are confidently wrong, and they change answers when a clinically equivalent question is rephrased. In a binary answer head both failures track the same logit margin, so we test how well one score screens for both. On MedGemma-4B-IT across MIMIC-CXR (in-distribution, n=196) and PadChest (out-of-distribution, n=861), single-pass predictive entropy predicts which yes/no answers flip under rephrasing with an area under the receiver operating characteristic curve (AUROC) of 0.823 on PadChest and 0.821 on MIMIC, and the signal is stable across five random seeds (0.828...
| Authors | Binesh Sadanandan, Vahid Behzadan |
|---|---|
| Primary category | cs.LG |
| Published | 10 April 2026 |
| Last updated at source | 11 August 2026 |
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