MULTI-LINGUAL VOICE AND TEXT ENABLED HOSPITAL CHATBOT FOR HEALTHCARE INFORMATION

Abstract

Hospitals frequently face challenges in delivering timely and accessible information to patients due to high inquiry volumes, language barriers, and limited staff availability. This paper proposes a multilingual, voice-enabled hospital chatbot that provides real-time assistance through both text and speech interfaces. The proposed system leverages Large Language Model (LLM)-based sentence embeddings using Sentence-BERT for semantic similarity-driven question answering, along with Google Translate API for multilingual support and Google Text-toSpeech for voice responses. The chatbot supports multiple Indian languages, including English, Hindi, Telugu, Tamil, Kannada, and Marathi, enabling inclusive communication across diverse user groups. Designed as a Flask-based web application with a responsive Bootstrap interface, the system aims to achieve effective contextual understanding, reduced response time, and improved accessibility when compared to traditional rule-based hospital inquiry systems. The proposed approach highlights the potential of LLM-driven semantic retrieval-based conversational agents in enhancing patient engagement and improving operational efficiency in healthcare environments.