Natural Language Processing in Healthcare: From Unstructured Data to Clinical Intelligence

Natural Language Processing (NLP) has emerged as a critical methodology for leveraging unstructured clinical text in modern healthcare systems. The recent advancements in deep learning, domain-specific language models, and the advent of large language models (LLMs) have greatly improved clinical information extraction, decision support, and automated documentation. However, these developments have raised new methodological and ethical issues which reduce the generalizability of the applications to the clinic and the adoption of the practices. This review provides a comprehensive and coherent examination of the development of NLP in healthcare, as well as clinical uses and implementation issues. We examine the performance of the various NLP paradigms in various clinical scenarios, and discuss common challenges like annotation bias, interpretability and safety issues related to generative models, and data heterogeneity. We do not give a descriptive inventory, but we stress analytical synthesis and clinical relevance. The review wraps up with a discussion on future research trends such as multimodal language modelling, federated learning, explainable AI and governance of LLMs. This review addresses the gap between methodological developments and clinical application, providing a valuable reference for the research and implementation of NLP-driven healthcare solutions.