The rapid advancement of Natural Language Processing (NLP) and Artificial Intelligence (AI) has created substantial opportunities for improving healthcare delivery, particularly in the realm of clinical decision support systems (CDSS). Context-aware NLP techniques are increasingly being integrated into healthcare settings to extract valuable insights from unstructured medical texts. The role of Software Engineering in building scalable and efficient AI-driven healthcare solutions is paramount, especially in handling APIs, Distributed Systems, and Image Processing for seamless data integration. This paper explores the intersection of AI-driven NLP and healthcare, with a focus on developing context-aware solutions to enhance clinical decision-making. It reviews existing methodologies, challenges, and future directions for improving the accuracy, efficiency, and usability of these systems, with implications for patient outcomes and healthcare providers.