<p>The rapid advancement of deep learning in medical imaging necessitates reliable out-of-distribution (OOD) detection to ensure safe clinical deployment and prevent potential patient harm. Traditional supervised methods, which rely on closed-world assumptions, struggle in real-world scenarios where abnormal samples are both diverse and scarce. To address this, we propose a novel synthetic outlier-based OOD detection framework that distinguishes OOD samples without requiring real abnormal data. Our method generates a diverse and structurally plausible set of pseudo-outliers through a hybrid synthesis pipeline combining local and global transformations. By incorporating these synthetic outliers into a constrained optimization framework during training, our model learns a more discriminative decision boundary between in-distribution (ID) and OOD samples. We conducted extensive evaluations on four diverse medical imaging datasets, and the results show that our approach significantly outperforms state-of-the-art techniques in medical OOD detection while preserving high in-distribution classification accuracy. Our findings demonstrate that strategically generating synthetic outliers provides an effective and practical solution for enhancing model robustness in safety-critical medical applications.</p>