Explainable AI in Clinical Decision Support Systems

Abstract

The increasing adoption of artificial intelligence in healthcare has improved diagnostic accuracy and efficiency, but the lack of transparency in complex models remains a major challenge. This paper presents an Explainable AI framework for Clinical Decision Support Systems aimed at enhancing interpretability and trust in AI-driven healthcare applications. The proposed system integrates machine learning models with explainability techniques such as feature importance analysis, SHAP values, and rule-based explanations to provide insights into model predictions. By making decision processes transparent, the framework enables clinicians to understand, validate, and trust AI recommendations. The approach also supports regulatory compliance and ethical considerations in healthcare systems. Additionally, integration with prior research in disease prediction and ensemble learning enhances the robustness and reliability of the system. Experimental observations indicate that the proposed framework maintains high predictive performance while significantly improving model interpretability. The study highlights the importance of explainable AI in bridging the gap between complex computational models and clinical practice, enabling safer and more effective decision-making in healthcare environments.