Quantitative Review of Conversational Agents in E-Health Using NLP and AI

Conversational agents have rapidly evolved, becoming a key element in human-machine interaction by leveraging natural language interfaces. These systems enable users to interact with technology in an intuitive and efficient manner, regardless of location, thanks to the proliferation of mobile devices and ubiquitous internet connectivity. Initially limited to rule-based systems, recent advances in artificial intelligence (AI), natural language processing (NLP), and machine learning (ML) have empowered conversational agents to deliver more complex and personalized user experiences. In the healthcare sector, conversational agents offer significant potential, assisting patients in managing medical conditions, scheduling appointments, and accessing vital health information. This article provides a comprehensive review of the state-of-the-art in healthcare applications of conversational agents, drawing from a thorough analysis of literature found in the Scopus and PubMed databases. By leveraging NLP and AI-driven methodologies to manage vast datasets, this study examines the technological foundations of conversational agents, such as natural language understanding (NLU), natural language generation (NLG), and machine learning techniques, while also exploring their practical implementations in healthcare. The paper concludes by presenting current challenges and outlining directions for future research in this rapidly growing field.