Purpose/Significance To develop a medical quality control system based on large language models (LLM), in order 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 techniques are 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.