EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR TRUSTWORTHY DECISION-MAKING IN HEALTHCARE SYSTEMS

Artificial Intelligence (AI) has significantly transformed healthcare by enabling automated diagnosis, predictive analytics, medical image interpretation, and personalized treatment recommendations. Despite remarkable advances, the lack of transparency in many AI models remains a major obstacle to widespread clinical adoption. Healthcare professionals require understandable and trustworthy explanations before relying on AI-assisted decisions. Explainable Artificial Intelligence (XAI) has emerged as a promising research field aimed at making machine learning models more transparent, interpretable, and accountable. This paper examines the role of XAI in healthcare systems, discussing its techniques, applications, advantages, challenges, and future research directions. The study reviews existing explainability methods, including model-specific and model-agnostic approaches, and evaluates their effectiveness in clinical environments. The paper concludes that explainability is essential for ethical, reliable, and human-centered AI systems that support medical professionals while improving patient outcomes.