Epistemic Uncertainty-Aware Defect Detection for Quality Control in Medical Device Manufacturing

arXiv cs.AIOctober 8, 2026en

arXiv:2610.09057v1 Announce Type: new Abstract: Objective: We investigate whether accounting for epistemic uncertainty can improve the reliability of automated defect detection in medical device manufacturing. Methods: We consider a machine learning framework that operates on heterogeneous manufacturing and device-report data represented with Knowledge Graphs. To mitigate errors arising from uncertainty in the decision model, we analyze a principled rejection strategy to abstain from predictions whose estimated epistemic uncertainty exceeds a specified threshold. We evaluate the approach using standard synthetic benchmarks and real-world medical device report data. Results: The theoretical r

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