Author:
Chen Haoran,Zhang Shengxiao,Zhang Lizhong,Geng Jie,Lu Jinqi,Hou Chuandong,He Peifeng,Lu Xuechun
Abstract
AbstractThe application of ChatGPTin the medical field has sparked debate regarding its accuracy. To address this issue, we present a Multi-Role ChatGPT Framework (MRCF), designed to improve ChatGPT's performance in medical data analysis by optimizing prompt words, integrating real-world data, and implementing quality control protocols. Compared to the singular ChatGPT model, MRCF significantly outperforms traditional manual analysis in interpreting medical data, exhibiting fewer random errors, higher accuracy, and better identification of incorrect information. Notably, MRCF is over 600 times more time-efficient than conventional manual annotation methods and costs only one-tenth as much. Leveraging MRCF, we have established two user-friendly databases for efficient and straightforward drug repositioning analysis. This research not only enhances the accuracy and efficiency of ChatGPT in medical data science applications but also offers valuable insights for data analysis models across various professional domains.
Funder
Sub-project of National Key R&D Program of China
Multi-center Clinical Research Project of National Clinical Research Center for Geriatric Diseases
Key Military Health Project
Clinical Decision-Making Research Big Data Shanxi Province Key Laboratory
National Natural Science Foundation of China
the Natural Science Foundation of Shanxi Province
National Social Science Fund of China
the Key R&D Program of Shanxi Province “Research on Key Technologies of Multi-source Data Drug Repositioning”
Publisher
Springer Science and Business Media LLC