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ORIGINAL RESEARCH article

Front. Med.
Sec. Family Medicine and Primary Care
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1460553
This article is part of the Research Topic Large Language Models for Medical Applications View all 4 articles

MED-ChatGPT CoPilot: A ChatGPT medical assistant for case mining and adjunctive therapy

Provisionally accepted
Wei Liu Wei Liu 1,2Hongxing Kan Hongxing Kan 1,2*Yanfei Jiang Yanfei Jiang 1,2*Yingbao Geng Yingbao Geng 1,2*Yiqi Nie Yiqi Nie 1,2Mingguang Yang Mingguang Yang 1,2*
  • 1 Anhui University of Chinese Medicine, Hefei, China
  • 2 School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, Anhui Province, China

The final, formatted version of the article will be published soon.

    Background: The large-scale language model, GPT-4-1106-preview, supports text of up to 128k characters, which has enhanced the capability of processing vast quantities of text. This model can perform efficient and accurate text data mining without the need for retraining, aided by prompt engineering. Method: The research approach includes prompt engineering and text vectorization processing. In this study, prompt engineering is applied to assist ChatGPT in text mining. Subsequently, the mined results are vectorized and incorporated into a local knowledge base. After cleansing 306 medical papers, data extraction was performed using ChatGPT. Following a validation and filtering process, 241 medical case data entries were obtained, leading to the construction of a local medical knowledge base. Additionally, drawing upon the Langchain framework and utilizing the local knowledge base in conjunction with ChatGPT, we successfully developed a fast and reliable chatbot. This chatbot is capable of providing recommended diagnostic and treatment information for various diseases. Results: The performance of the designed ChatGPT model, which was enhanced by data from the local knowledge base, exceeded that of the original model by 7.90% on a set of medical questions. Conclusion: ChatGPT, assisted by prompt engineering, demonstrates effective data mining capabilities for large-scale medical texts. In the future, we plan to incorporate a richer array of medical case data, expand the scale of the knowledge base, and enhance ChatGPT's performance in the medical field.

    Keywords: ChatGPT, Large Language Model, Data Mining, Prompt Engineering, local knowledge base

    Received: 06 Jul 2024; Accepted: 03 Oct 2024.

    Copyright: © 2024 Liu, Kan, Jiang, Geng, Nie and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Hongxing Kan, Anhui University of Chinese Medicine, Hefei, China
    Yanfei Jiang, Anhui University of Chinese Medicine, Hefei, China
    Yingbao Geng, Anhui University of Chinese Medicine, Hefei, China
    Mingguang Yang, Anhui University of Chinese Medicine, Hefei, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.