Advances and Applications of Natural Language Processing in Healthcare

Natural Language Processing (NLP) has emerged as a transformative technology in the healthcare sector, offering tools to efficiently analyze and extract insights from the vast amount of unstructuredclinical data. This reviewpresents a comprehensive examination of recent advancements in NLP applications within healthcare, covering a spectrum of approaches from early symbolic and statistical models to state-of-the-art deep learning techniques, including Transformerarchitecturessuchas BERT, Clinical BERT,and Med-BERT. These models have significantly enhanced performance in various healthcare-related NLP tasksdue to their ability to capture contextual semantics and medical- specific language features. Key application areas discussed include electronic health record (EHR) mining, clinical decision support, medical coding, sentiment analysis in patient feedback, and disease prediction. NLP methods are increasingly used to support diagnosis, automate clinical documentation, monitor public healthtrends,andfacilitatepersonalizedtreatmentplanning. Despite theirpromise, several methodological and practical challenges remain. These include limited annotated medical data,domainspecificterminologyvariation,privacy,ethical concerns, explainability of model outputs, and integration into existing healthcare systems. This review synthesizes insights from over 30 studies to identify current trends and potential research gaps. It highlights the growing shift toward interpretable and explainable NLP models, especially in high-stakes clinical environments. Furthermore, the paper underscores the importance of interdisciplinary collaboration between computer scientists and healthcare professionals to ensure responsible and effective deployment. Future work must focus on building robust, transparent, and scalable NLP systems that can be reliably integrated into diverse healthcare workflows, particularly in resource-constrained settings. 4,5,8