Evaluating the Efficacy of AI Chatbots for Patient Engagement using Generative AI and Large Language Models

Abstract

The rapid advancement of Generative AI and Large Language Models (LLMs) has significantly transformed digital healthcare systems, particularly in enhancing patient engagement through intelligent chatbot solutions. This study evaluates the effectiveness of LLM-powered AI chatbots in improving patient interaction, accessibility, and overall healthcare experience. The proposed system leverages advanced natural language processing techniques to provide personalized, real-time responses to patient queries, enabling continuous communication and support. Key performance metrics such as response accuracy, user satisfaction, engagement rate, and system efficiency are analyzed to assess chatbot performance. Experimental results demonstrate that AI-driven chatbots outperform traditional rule-based systems in terms of adaptability, contextual understanding, and scalability. Furthermore, the integration of Generative AI enables dynamic conversation generation, improving user trust and interaction quality. The findings suggest that LLM-based chatbots can play a crucial role in modern healthcare by reducing workload on medical professionals while enhancing patient-centered care. This research highlights the potential of AI chatbots as a scalable and efficient solution for improving healthcare engagement.