Natural language processing for healthcare: Applications, progress, and future directions - Rahim, Fakher; Abdulkareem Hameed, Nada; Abdulfattah Salih, Saja; Mahmood Jawad, Aqeel; Mahmood Salman, Hayder; Chornomordenko, Dmytro (Edelweiss Applied Science and Technology, 2024)
Natural Language Processing (NLP) is causing a significant change in the healthcare industry. This state-of-the-art technology is transforming the processing, analysis, and utilisation of healthcare data by improving patient care and clinical decision-making. This article aims to analyse the current and prospective uses of natural language processing (NLP) in the healthcare industry. We examine the tangible advancements that have been made and shed light on the bright prospects that lie ahead. The approach for early sickness diagnosis involves the use of advanced predictive analytics based on natural language processing (NLP). By analysing unstructured patient narratives, models may accurately identify potential health issues with exceptional precision, sometimes exceeding 90% accuracy. This article discusses the benefits of using voice recognition and chatbots, such as improved patient-provider communication and decreased administrative tasks. Systems powered by natural language processing have made significant advancements, as shown by statistical data indicating that they attain an accuracy rate of 80% or more on tasks like clinical text classification. Consequently, medical record review and data extraction have been automated, hence alleviating the burden on healthcare personnel. Hospitals may use sentiment analysis on online reviews to assess patient satisfaction levels and implement targeted improvements, therefore enhancing the overall patient experience. Undoubtedly, natural language processing (NLP) is revolutionizing the healthcare industry by enabling data-driven insights to optimise operations, personalize patient therapy, and enhance overall patient care quality. In order to fully harness the capabilities of natural language processing, we must address existing challenges and promote continuous research in areas such as deep learning and explainability. Only by doing so can we establish a healthcare system that is both more health-oriented and reliant on data.