חשוב מחדש על נכונות להערכת הפנמה בתחום הצפיות הרפואיות עם מודלי שפה-תמונה

תקציר מקורי באנגליתarXiv:2609.15180v1 Announce Type: new Abstract: 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 agreement with human judgements and fidelity to human-referenced UE performance. We assess eight criteria across three clinical prediction tasks and three mode