AI RESEARCH
Risk-Calibrated Learning: Minimizing Fatal Errors in Medical AI
arXiv CS.CV
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ArXi:2604.12693v1 Announce Type: new Deep learning models often achieve expert-level accuracy in medical image classification but suffer from a critical flaw: semantic incoherence. These high-confidence mistakes that are semantically incoherent (e.g., classifying a malignant tumor as benign) fundamentally differ from acceptable errors which stem from visual ambiguity. Unlike safe, fine-grained disagreements, these fatal failures erode clinical trust.