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

Vollständiger Abstract

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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.