Explainable Artificial Intelligence (XAI) has emerged as a critical field in modern healthcare, addressing the limitations of traditional "black-box" AI systems that lack transparency and interpretability. Your project focuses on developing an interpretable AI framework to assist clinicians in diagnosis, treatment decision-making, and patient management. This review summarizes the motivation, existing literature, research gaps, methodological framework, and potential clinical impact, while also interpreting the conceptual diagrams provided. The work highlights how XAI can improve clinician trust, ensure accountability, reduce bias, and enhance patient outcomes by making AI decisions understandable and actionable.