Voice and Text-Based Healthcare Chatbot with Real-Time Multilingual Translation - Sydulu , S. k. John; Jyothi , Koppula Naga; Chandana, Pavuluri Naga; Sree Enosh , Katru Vijaya; Gopi, Pallapu (International Journal of Recent Advances in Engineering and Technology, 2025)
The integration of artificial intelligence (AI) in healthcare has paved the way for advanced, accessible, and efficient patient support systems. This paper presents a multilingual, AI-powered medical chatbot designed to predict and respond to infectious disease-related queries using a deep learning approach. The chatbot leverages Long Short-Term Memory (LSTM) networks to process and interpret user inputs in both text and voice formats. Trained on a COVID-19-focused question dataset, the model achieves a high training accuracy of 99%, demonstrating strong predictive capabilities. To overcome language barriers, the chatbot integrates Google Translate API, enabling real-time multilingual communication in English and Telugu, with potential for future language expansion.The system architecture includes modules for user authentication, voice and text interaction, NLP-based processing, multilingual translation, and chat history retrieval. A cloud-based backend ensures scalability, secure data handling, and rapid response generation. The chatbot provides medically relevant responses to users in real-time, enhancing accessibility in remote and multilingual populations. Experimental evaluation confirms the model’s effectiveness across accuracy, response time, and language adaptability. This research contributes to AI-driven healthcare by proposing a scalable and privacy-conscious solution that supports multilingual, multimodal interaction. Future enhancements include expanding language support, integrating with electronic health records (EHRs), and improving contextual understanding through advanced NLP and feedback learning.