Uc- PrUn : Uncertainty-Calibrated Machine Unlearning using Vision–Language Models for Clinical Decision Support

In this study, we introduce Uc-PrUn , a principled framework designed to improve the reliability of Vision–Language Models (VLMs) in clinical decision-support systems. Recognizing the critical importance of uncertainty estimation in high-stakes domains, we introduce a two-stage methodology; the first stage focuses on Bayesian-inspired zero-shot uncertainty quantification using Monte Carlo dropout, while the second stage introduces a novel uncertainty-aware machine unlearning strategy, termed Uc-PrUn . Leveraging the Harvard-FairVLMed dataset, which comprises paired SLO fundus images and clinical notes for glaucoma detection, we systematically evaluate various VLMs to quantify epistemic uncertainty and identify high-variance training samples. Our pruning and unlearning mechanism selectively removes uncertain samples to enhance model calibration and improve downstream performance. Experimental results demonstrate that the Uc-PrUn approach not only reduces predictive uncertainty but also yields consistent gains in accuracy and F 1 -scores across multiple Vision–Language Models. These findings support incorporating uncertainty-aware pruning mechanisms in medical AI pipelines where model reliability is essential.