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Medical AI Encodes a "Feeling of Error": Verifying Cancer Segmentation via Internal Concepts
Cancer segmentation models can fail silently, generating plausible but incorrect masks that risk missed findings or unnecessary biopsies. A critical question arises: Do AI models "know" when they are wrong, and if so, can we use the signal to predict their own failures? Humans do have a "Feeling of Error" (FOE): a spontaneous sense of unease that flags a potential error during thinking. We investigate whether cancer segmentation models exhibit an analogous internal signal. Unlike output-level cues (e.g., prediction confidence or uncertainty), which offer no insight into why a failure occurs an
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-08T15:21:08.000Z
First collected: 2026-09-20T20:02:11.508Z. This is not the publication date.