Healthcare Interaction Through Conversational AI

As the healthcare industry increasingly relies on AI-driven solutions, there is a growing need for conversational agents that can efficiently address patient queries while providing accurate and context-sensitive responses. Traditional models, based solely on rule-based or deep learning approaches, often lack the necessary adaptability and precision. To address this challenge, a novel hybrid model has been developed, integrating Long Short-Term Memory networks and Transformer architecture with predefined rule-based strategies. Additionally, feature extraction and sentiment analysis were incorporated to enhance the quality of interactions and emotional intelligence. This hybrid model has outperformed existing healthcare conversational agents, achieving impressive metrics: Accuracy of 98.17%, Precision of 98.07%, Recall of 98.17%, and an F1 Score of 97.93%. These results demonstrate that by combining rule-based strategies with advanced deep learning techniques, the effectiveness of healthcare conversational agents can be significantly improved, ultimately enhancing patient care and engagement.