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

In this study, we introduce

, 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 . 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 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.