A Systematic Review of NLP Applications in Clinical Healthcare: Advancement and Challenges

This systematic literature review examines the advancements and challenges of natural language processing applications in clinical healthcare. Authors provide an overview of NLP applications, including clinical text classification, named entity recognition, information extraction, clinical dialogue systems, and clinical decision support. These applications have improved clinical documentation, patient care, and research outcomes. Authors critically evaluate challenges such as data privacy, lack of standardized datasets, and domain-specific language models. Ethical considerations, interoperability, and potential biases in NLP algorithms are also discussed. This review highlights the current state of NLP in clinical healthcare, identifies areas for improvement, and suggests future research directions. By synthesizing existing literature, this paper contributes to a deeper understanding of NLP’s potential in transforming clinical practice.