Current status and outlooks of acupuncture research driven by machine learning.
10.13703/j.0255-2930.20240809-k0002
- Author:
Sixian WU
1
;
Linna WU
2
;
Yi HU
3
;
Zhijie XU
1
;
Fan XU
4
;
Hanbo YU
1
;
Guiping LI
4
Author Information
1. Department of Acupuncture and Moxibustion, First Teaching Hospital of Tianjin University of TCM, Tianjin 300193, China; National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin 300193; Graduate School, Tianjin University of TCM, Tianjin
2. First Clinical Medical School, Yunnan University of CM.
3. Graduate School, Tianjin University of TCM, Tianjin
4. Department of Acupuncture and Moxibustion, First Teaching Hospital of Tianjin University of TCM, Tianjin 300193, China; National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin
- Publication Type:Journal Article
- Keywords:
acupuncture;
artificial intelligence;
current status;
machine learning;
outlook
- MeSH:
Acupuncture Therapy/trends*;
Machine Learning;
Humans;
Algorithms
- From:
Chinese Acupuncture & Moxibustion
2025;45(4):421-427
- CountryChina
- Language:Chinese
-
Abstract:
The machine learning is used increasingly and widely in acupuncture prescription optimization, intelligent treatment and precision medicine, and has obtained a certain achievement. But, there are still some problems remained to be solved such as the poor interpretability of the model, the inconsistency of data quality of acupuncture research, and the clinical application of constructed models. Researches in future should focus on the acquisition of high-quality clinical and experimental data sets, take various machine learning algorithms as the basis, and construct professional models to solve various problems, so as to drive the high-quality development of acupuncture research.
