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Clinical documentation is crucial for patient care. However, the problem lies in the process being time consuming and error-prone. This paper presents a novel approach to improving clinical documentation through the development of a Medical AI Chatbot powered by natural language processing (NLP) and large language models (LLMs). The chatbot utilizes an advanced AI model, Zephyr 7b Beta, to interact with clinical data. The chatbot interacts with a dataset of patient records, retrieving relevant information. The paper details the methodology used to develop the chatbot, including data preprocessing, embedding generation, model integration and query processing. By leveraging NLP techniques, the chatbot demonstrates its ability to provide accurate and context specific information to healthcare professionals through Retrieval Augmented Generation (RAG) technique.

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Leveraging NLP and Large Language Models for Clinical Documentation Improvement: A Medical AI Chatbot Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Bhavraaj Singh,
  • Atif Farid Mohammad,
  • Muhammad Abdul Basit Ur Rahim

摘要

Clinical documentation is crucial for patient care. However, the problem lies in the process being time consuming and error-prone. This paper presents a novel approach to improving clinical documentation through the development of a Medical AI Chatbot powered by natural language processing (NLP) and large language models (LLMs). The chatbot utilizes an advanced AI model, Zephyr 7b Beta, to interact with clinical data. The chatbot interacts with a dataset of patient records, retrieving relevant information. The paper details the methodology used to develop the chatbot, including data preprocessing, embedding generation, model integration and query processing. By leveraging NLP techniques, the chatbot demonstrates its ability to provide accurate and context specific information to healthcare professionals through Retrieval Augmented Generation (RAG) technique.