The field of Artificial Intelligence (AI) focuses on making of transparent, comprehensible, and reliable models. The core of this chapter focuses on the transformative potential of XAI in linking impressive algorithms and human understanding, giving birth to a new era of dependable and ethical AI-guided healthcare. This capter contextualizes the rise of AI in healthcare with the evolution from rule-based systems to modern machine learning and deep learning models. The opaque black box algorithms have challenged clinician’s trust and patient safety, and understanding interpretability which is mandated regarding regulatory compliance. The main sections are a comprehensive overview of AI techniques including post-hoc explanations, interpretable model architectures, and hybrid methods. Real use cases illustrate these tools employed in building patient trust in clinical decision support systems. The chapter also discusses the technical, ethical, and regulatory obstacles to embedding AI in health care, like the need to balance model performance with its explicability, to reduce any bias in models, to ensure data protection, and to navigate evolving AI legislation compliance.