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In today’s huge market, we are seeing the advent of chatbots in every field be it e-commerce, tech, and medical field. This paper aims to build a chatbot using the Retrieval-Augmented Generation (RAG) Framework, which is able to detect a user’s disease and give proper medical advice holding an interactive conversation with the user knowing the kind of symptoms they are experiencing at that moment. Often, individuals delay seeking medical attention due to uncertainty or limited access. Our chatbot empowers users to quickly assess symptoms and take prompt action, guiding them toward the right type of care and improving their chances for better health outcomes. Our chatbot is designed to serve as an initial line of defense, offering users a reliable tool to recognize symptoms and take the first steps toward appropriate care. It uses the RAG pipeline and the Llama model to construct factually correct medical advice retrieved from the specially created Knowledge Base containing various medical documents, books, journals, and so on.

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MedMate: A Contextual Approach for Disease Diagnosis Using Retrieval-Augmented Generation

  • 会议论文

  • 发表日期：

    2025年

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  • Avhishek Nandi,
  • Barnali Paul,
  • Piyali Datta,
  • Deepsubhra Guha Roy

摘要

In today’s huge market, we are seeing the advent of chatbots in every field be it e-commerce, tech, and medical field. This paper aims to build a chatbot using the Retrieval-Augmented Generation (RAG) Framework, which is able to detect a user’s disease and give proper medical advice holding an interactive conversation with the user knowing the kind of symptoms they are experiencing at that moment. Often, individuals delay seeking medical attention due to uncertainty or limited access. Our chatbot empowers users to quickly assess symptoms and take prompt action, guiding them toward the right type of care and improving their chances for better health outcomes. Our chatbot is designed to serve as an initial line of defense, offering users a reliable tool to recognize symptoms and take the first steps toward appropriate care. It uses the RAG pipeline and the Llama model to construct factually correct medical advice retrieved from the specially created Knowledge Base containing various medical documents, books, journals, and so on.