EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR SECURE HEALTHCARE DECISION SUPPORT SYSTEMS

Artificial Intelligence (AI) has significantly transformed healthcare by enabling intelligent clinical decision support, disease prediction, medical image interpretation, and personalized treatment planning. However, many AI models function as "black boxes," making it difficult for healthcare professionals to understand the reasoning behind predictions. Explainable Artificial Intelligence (XAI) addresses this limitation by providing transparent and interpretable decision-making processes. This paper explores the integration of XAI into secure healthcare decision support systems, highlighting recent explainability techniques, security mechanisms, implementation frameworks, applications, challenges, and future research opportunities. The study concludes that combining explainability with cybersecurity and privacy-preserving technologies enhances trust, accountability, and regulatory compliance while improving clinical outcomes.