Construction and application of the "Huaxi Hongyi" large medical model
- VernacularTitle:“华西黉医”大模型构建与应用
- Author:
Rui SHI
1
;
Bing ZHENG
1
;
Xun YAO
1
;
Hao YANG
1
;
Xuchen YANG
1
;
Siyuan ZHANG
1
;
Zhenwu WANG
1
;
Dongfeng LIU
1
;
Jing DONG
1
;
Jiaxi XIE
1
;
Hu MA
2
;
Zhiyang HE
3
;
Cheng JIANG
4
;
Feng QIAO
5
;
Fengming LUO
6
;
Jin HUANG
7
Author Information
1. Department of Information, West China Hospital of Sichuan University, Chengdu, 610041, P. R. China
2. ICT Product Portfolio Management and Solution Department, Huawei Technologies Co., Ltd, Chengdu, 610041, P. R. China
3. iFLYTEK Healthcare Research Institute, iFLYTEK Healthcare Technology Co., Ltd, Hefei, 230031, P. R. China
4. Sichuan Branch, China Telecom Co., Ltd, Chengdu, 610041, P. R. China
5. Research and Development Department 1, China Mobile (Chengdu) Information and Communication Technology Co., Ltd, Chengdu, 610213, P. R. China
6. Department of Respiratory and Critical Care Medicine/Clinical Research Center for Respiratory Disease/Laboratory of Pulmonary Immunology and Inflammation/Department of High Altitude Medicine, Center for High Altitude Medicine, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China
7. Medical Device Regulatory Research and Evaluation Center/Sichuan Provincial Comprehensive Clinical Center for Public Health, West China Hospital of Sichuan University, Chengdu, 610041, P. R. China
- Publication Type:Journal Article
- Keywords:
Medical large model;
data annotation;
multimodal learning;
medical record generation;
articficial intelligence
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2025;32(05):587-593
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.
