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Rethinking Correctness for Uncertainty Estimation in Clinical Prediction with Vision-Language Models

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

Vision-language models are increasingly explored for clinical prediction from electronic health records and medical images, where identifying unreliable predictions is important for safe deployment. Uncertainty estimation (UE) enables detecting such predictions, but its evaluation depends on a correctness criterion that determines whether each model output is correct. If this criterion disagrees with human judgement or distorts downstream UE performance, conclusions about model reliability can be misleading. We introduce a two-axis framework that evaluates correctness criteria by their agreeme

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