MedBayes-Lite: A Clinical Uncertainty Governance Layer for Risk-Aware Medical Decision Support
Elias Hossain; Md Mehedi Hasan Nipu; Maleeha Sheikh; Tasfia Nuzhat; Rajib Rana; Subash Neupane; Björn W. Schuller; Niloofar Yousefi · 2025 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Clinical language models often assign high confidence to incorrect predictions, particularly in high-severity and out-of-distribution cases. We present MedBayes-Lite, a retraining-free uncertainty governance layer for transformer-based clinical predictors. It combines Monte Carlo dropout, predictive calibration, and confidence-guided abstention to defer low-confidence predictions for human review, adding no trainable parameters. Evaluated on MedMCQA and MedQA-USMLE, MedBayes-Lite reduces expected calibration error by 0.23 to 0.33 and drives harmful overconfident errors (confident, incorrect, h
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Metadata source: arXiv