While artificial intelligence is rapidly evolving to transform healthcare, many of its strongest models remain fundamentally opaque, or “black-box” models. Without understanding why a certain output has been produced, how can clinicians and patients place their trust in or react responsibly to AI-based recommendations? This chapter outlines the case for XAI as vital in closing the gap between algorithm performance and the clinical adoption of such systems. We start with definitions of some basic concepts: interpretability, transparency, and explainability. These further distinguish between local and global explanations, as well as ante-hoc and post-hoc approaches. Deeply entrenched methods, such as LIME, SHAP, and Grad-CAM, are presented as useful tools to elicit the reasons behind model predictions. Equally important is the evaluation point: What should be weighted most—faithfulness to the underlying model, robustness across cases, or clinical relevance? With respect to this, emerging frameworks, such as BenchXAI and CLIX-M, together with the idea of XAI harmonizer, demonstrate how explanations can be assessed systematically and integrated across unimodal/multimodal data sources. Applied examples show how much XAI has already changed healthcare: indicating salient regions in medical imaging, identifying predictive features in risk stratification, and shaping physician trust in clinical decision support systems. Broader implications for precision medicine, drug discovery, and public health are also explored, under the ethical and regulatory panorama (EU AI Act, FDA guidance, data protection regulations). This chapter finally concludes that explainable AI should no longer be regarded as an add-on but rather as an essential prerequisite for safe, transparent, and human-centered healthcare innovation. The fact that XAI makes the decisions of AI interpretable fosters trust, enhances accountability, and ensures that intelligent systems are responsibly integrated into medical practice.