P. HIMA BINDU,MUNDRAI HIMABINDHU,M SHARANYA,MOHAMMAD SOHEL,TUMMIDI ASHWIDH 03 03 Ensuring patient safety is a critical concern in healthcare where errors or adverse events can have severe consequences Traditional predictive models often provide limited insight into their decisionmaking processes making it challenging for healthcare professionals to trust and act on their recommendations Explainable Artificial Intelligence XAI offers a solution by providing transparent and interpretable models that highlight the reasoning behind predictions This paper presents an XAIbased approach to enhance patient safety by analyzing clinical data to predict potential risks such as medication errors adverse drug reactions or hospitalacquired infections By combining machine learning algorithms with interpretability techniques like SHAP Shapley Additive Explanations and LIME Local Interpretable Modelagnostic Explanations the system not only predicts safety risks but also explains the contributing factors for each prediction Experimental results on healthcare datasets demonstrate that the proposed system achieves high predictive accuracy while providing clear actionable explanations for clinicians This transparency improves trust facilitates timely intervention and supports informed decisionmaking ultimately enhancing patient safety and care quality Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026