A Framework for Explainable Artificial Intelligence in Healthcare Using Model-Agnostic Methods

The growing use of the artificial intelligence (AI) in clinical decision support systems has compounded the necessity of transparent and reliable models with the ability to give reliable explanations. The conventional deep learning and ensemble-based healthcare prediction models have high accuracy, but they are black boxes and their use may be restricted in regulated clinical settings. The current paper suggests a unified model-agnostic Explainable Artificial Intelligence (XAI) system that will combine multimodal data processing with stratified interpretability to improve clinical transparency. The framework is comprised of local methods of explaining, including SHAP and LIME, global interpretability using aggregated SHAP values and permutation importance, and actionable explanation using counterfactual analysis and contrastive analysis. The experimental assessment based on the use of structured EHR data, physiological measures, and medical imaging reveals that the suggested system is highly predictive and produces clinically significant explanations. The findings indicate better model traceability, adherence to medical reasoning patterns and more support in risk stratification, treatment guidance and regulatory adherence. The suggested framework offers a growth step towards implementing interpretable AI systems in practice in healthcare.