Abstract:Medical Vision Language Models VLMs suffer from two failure modes that threaten safe deployment mis calibrated confidence and sensitivity to question rephrasing. We show they share a common cause, proximity to the decision boundary, by benchmarking five uncertainty quantification methods on MedGemma 4BIT across in distribution MIMIC CXR and outof distribution PadChest chest X ray datasets, with cross architecture validation on LLaVA RAD7B. For well calibrated single model methods, predictive entropy from one forward pass predicts which samples will flip under rephrasing AUROC 0.711 on MedGemma, 0.878 on LLaVARAD p 10 4, enabling a single entropy threshold to flag both unreliable and rephrase sensitive predictions. A five member LoRA ensemble fails under the MIMIC PadChest shift 42.9 ECE, 34.1 accuracy, though LLaVA RAD s ensemble does not collapse 69.1. MC Dropout achieves the best calibration ECE 4.3 and selective prediction coverage 21.5 at 5 risk, yet total entropy from a single forward pass outperforms the ensemble for both error detection AUROC 0.743 vs 0.657 and paraphrase screening. Simple methods win.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.08941 [cs.LG] |
| (or arXiv:2604.08941v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.08941 arXiv-issued DOI via DataCite (pending registration) |
From: Binesh Kumar Sadanandan [view email]
[v1]
Fri, 10 Apr 2026 04:18:47 UTC (105 KB)
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