Towards Intelligent Healthcare Delivery: A Reinforcement Learning-Enhanced Conversational Agent for Medical Consultation & Patient Engagement

This paper presents MediBot, a reinforcement learning-based conversational agent developed for intelligent healthcare consultation and continuous patient engagement. The system integrates advanced natural language understanding methods, including intent recognition through contextual semantic parsing, biomedical entity identification using ontology-guided lexical models, and sentiment analysis via polarity-weighted embeddings. These components operate with a Deep Q-Network policy optimizer that refines dialogue strategies through temporal difference learning and experience replay. MediBot enables coherent, multi-turn medical interactions through adaptive response generation informed by state–action value estimation. Experimental evaluation across intent accuracy, entity extraction precision, sentiment inference, policy convergence, and user engagement demonstrates superior performance over existing healthcare dialogue systems. The results confirm stable policy learning, real-time response capability, and sustained user satisfaction. This work advances computational healthcare by establishing an effective integration of reinforcement learning and domain-specific language understanding for scalable and personalized medical dialogue systems.