Construction of a Medical Quality Control Application System Based on Large Language Models

Corresponding author: YE Qi, yeh_qi1125@ecust.edu.cn

DOI: 10.12201/bmr.202503.00004

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  Abstract: Purpose/Significance This study aims to develop a medical quality control system based on large language models (LLMs) to enhance the automation and accuracy of quality control processes. Method/Process The system utilizes a diverse set of high-quality medical data, including general medical data and quality control-related data, to fine tune the PULSE model. Automated indicator calculation and retrieval-augmented generation (RAG) techniques are then employed to enable the system to efficiently perform complex quality control tasks. Result/Conclusion The system demonstrates significant improvements in automated computation and inference accuracy, achieving an accuracy of 93.31%. It outperforms baseline language models and common inference methods, offering significant implications for medical quality control.

  Key words: Large Language Models, Instruction Fine-Tuning, Medical Quality Control; ; 

  Submit time: 3 March 2025

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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  1	2024-12-25	bmr.202503.00004V1	Download

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