Explainable AI Model for Transparent Decision-Making in Healthcare Systems

Artificial Intelligence (AI) has significantly transformed healthcare by enabling accurate diagnosis, predictive analytics, and personalized treatment planning. However, the “black-box” nature of many AI models raises concerns regarding trust, accountability, and interpretability, especially in critical medical decision-making scenarios. This paper proposes an Explainable AI (XAI) model designed to enhance transparency and reliability in healthcare systems. The proposed approach integrates machine learning techniques with interpretability methods such as feature importance analysis and model-agnostic explanation frameworks to provide clear insights into decision outcomes. By making AI predictions understandable to healthcare professionals, the model supports informed decision-making, improves patient trust, and ensures regulatory compliance. Experimental evaluation on healthcare datasets demonstrates that the proposed model maintains high accuracy while significantly improving explainability. The results highlight the potential of XAI in bridging the gap between complex AI systems and human-centric healthcare applications.