The Conversational Agents and Natural Language Processing: Artificial Intelligence in Medicine - Srikong, Mathuwan; Thuangtong, Atiporn; Prasongmanee, Chutirut (Journal of Roi Kaensarn Academi, 2024)
Background and Objective: The adoption of digital technology to enhance service delivery represents a significant advancement in the medical field. Digital Assistance Systems are gaining prominence, with Artificial Intelligence (AI) expected to play a pivotal role in transforming doctor-patient communication in the future. Advances in conversational agents equipped with natural language processing (NLP) capabilities are particularly compelling and are increasingly applied in diverse areas, including medical education. This review article explores these developments in detail, focusing on the integration of AI-driven conversational tools. Materials and Methods: This review article outlines a structured methodology for conducting a literature review and critiquing the integration of artificial intelligence (AI) tools with conversational agents and natural language processing in educational contexts. The goal is to assess their potential in enhancing knowledge acquisition, skill development, and understanding of complex medical concepts. Results: The integration of AI tools in learning has demonstrated significant potential in improving knowledge acquisition, skill development, and comprehension of medical concepts. These tools have been implemented in various forms, including 1) Medical information chatbots 2) Appointment scheduling chatbots 3) Medication management chatbots 4) Chatbots as mental health support 5) Post-discharge follow-up chatbots 6) Health Insurance guidance chatbots 7) Chronic disease management chatbots Conclusion: Conversational agents and Natural Language Processing (NLP) are groundbreaking technologies revolutionizing the medical field. They enhance diagnostic processes, streamline healthcare delivery, and support the development of clinical skills. However, it is crucial to prioritize accuracy, reliability, and validity in their application. Moreover, safeguarding sensitive data through robust privacy and security measures is essential to ensure the ethical use of AI in healthcare.