The rapid growth of advanced Natural Language Processing (NLP), especially deep learning models like BERT, is fundamentally changing healthcare. These technologies are essential for extracting vital insights from the immense amount of unstructured patient data, which constitutes a large portion of all healthcare information. While these systems achieve unprecedented accuracy in tasks from disease prediction to documentation automation, there is a cost of transparency. Their complex design makes their internal reasoning opaque, turning them into inscrutable black boxes. This lack of interpretability is a major obstacle to their adoption in critical clinical environments, where decisions profoundly affect human lives and require trust, verification, and clear accountability. This chapter focuses on Explainable NLP (XNLP), the crucial field dedicated to clarifying these models and establishing them as transparent, reliable partners in the healthcare ecosystem. We organize the many explanation methods into a foundational taxonomy based on their scope, timing, and generality. We explore key techniques, including feature attribution methods like LIME and SHAP, which quantify how individual input words contribute to a prediction, attention mechanisms to visualize the model’s focus, and rationalization for generating human-readable justifications. We stress the central requirement of faithfulness, which is ensuring explanations truly reflect the model’s internal logic, rather than just offering a plausible but misleading narrative. This requires strong evaluation, combining technical measurements of faithfulness with human assessments to ensure explanations are both technically correct and practically useful to clinicians. We demonstrate applications across a wide array of healthcare use cases, illustrating their practical impact on clinical work, such as automating medical coding and enhancing decision support for diagnosis. This chapter pays attention to examining the critical ethical, legal, and practical necessities driving XNLP, including patient data privacy, mitigating algorithmic bias for fairness, and establishing clear accountability. Explainability is not optional; it is essential for safe human-AI teamwork, securing patient safety, and ensuring NLP’s potential responsibly and equitably improves human health.