Evidence map›Paper›PMID 41333020›Full record

ArticleFrontiers in pharmacology2025

SZBC-AI4TCM: a comprehensive web-based computing platform for traditional Chinese medicine research and development.

Jidong Lang, Kaimin Guo, Jinna Yang, Pengcheng Yang, Yu Wei, Jingwen Han, Shuang Zhao, Zhihong Liu, Haowei Yi, Xin Yan and 11 more

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Jidong LangTianjin Tasly Digital Intelligence Chinese Medicine Technology Co., Ltd., Tianjin, China.
Kaimin GuoTianjin Tasly Digital Intelligence Chinese Medicine Technology Co., Ltd., Tianjin, China.
Jinna YangTianjin Tasly Digital Intelligence Chinese Medicine Technology Co., Ltd., Tianjin, China.
Pengcheng YangTianjin Tasly Digital Intelligence Chinese Medicine Technology Co., Ltd., Tianjin, China.
Yu WeiTianjin Tasly Digital Intelligence Chinese Medicine Technology Co., Ltd., Tianjin, China.
Jingwen HanTianjin Tasly Digital Intelligence Chinese Medicine Technology Co., Ltd., Tianjin, China.
Shuang ZhaoTianjin Tasly Digital Intelligence Chinese Medicine Technology Co., Ltd., Tianjin, China.
Zhihong LiuWecomput Technology Co., Ltd., Beijing, China.
Haowei YiWecomput Technology Co., Ltd., Beijing, China.
Xin YanWecomput Technology Co., Ltd., Beijing, China.
Binbin ChenTianjin Tasly Digital Intelligence Chinese Medicine Technology Co., Ltd., Tianjin, China.
Cheng WangTianjin Tasly Digital Intelligence Chinese Medicine Technology Co., Ltd., Tianjin, China.
Jian XuTianjin Tasly Digital Intelligence Chinese Medicine Technology Co., Ltd., Tianjin, China.
Jiawei GeTianjin Tasly Digital Intelligence Chinese Medicine Technology Co., Ltd., Tianjin, China.
Wen ZhangTianjin Tasly Digital Intelligence Chinese Medicine Technology Co., Ltd., Tianjin, China.
Xuezhong ZhouDepartment of Artificial Intelligence, Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science & Technology, Beijing Jiaotong University, Beijing, China.
Jiansong FangState Key Laboratory of Traditional Chinese Medicine Syndrome, Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China.
Jing SuTianjin Tasly Digital Intelligence Chinese Medicine Technology Co., Ltd., Tianjin, China.
Kaijing YanTianjin Tasly Digital Intelligence Chinese Medicine Technology Co., Ltd., Tianjin, China.
Yunhui HuTianjin Tasly Digital Intelligence Chinese Medicine Technology Co., Ltd., Tianjin, China.
Wenjia WangTianjin Tasly Digital Intelligence Chinese Medicine Technology Co., Ltd., Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: In recent years, the increasing complexity and volume of data in traditional Chinese medicine (TCM) research have rendered the conventional experimental methods inadequate for modern TCM development. The analysis of intricate TCM data demands proficiency in multiple programming languages, artificial intelligence (AI) techniques, and bioinformatics, posing significant challenges for researchers lacking such expertise. Thus, there is an urgent need to develop user-friendly software tools that encompass various aspects of TCM data analysis. Methods: We developed a comprehensive web-based computing platform, SZBC-AI4TCM, a comprehensive web-based computing platform for traditional Chinese medicine that embodies the "ShuZhiBenCao" (Digital Herbal) concept through artificial intelligence, designed to accelerate TCM research and reduce costs by integrating advanced AI algorithms and bioinformatics tools. Results: Leveraging machine learning, deep learning, and big data analytics, the platform enables end-to-end analysis, from TCM formulation and mechanism elucidation to drug screening. Featuring an intuitive visual interface and hardware-software acceleration, SZBC-AI4TCM allows researchers without computational backgrounds to conduct comprehensive and accurate analyses efficiently. By using the TCM research in Alzheimer's disease as an example, we showcase its functionalities, operational methods, and analytical capabilities. Discussion: SZBC-AI4TCM not only provides robust computational support for TCM research but also significantly enhances efficiency and reduces costs. It offers novel approaches for studying complex TCM systems, thereby advancing the modernization of TCM. As interdisciplinary collaboration and cloud computing continue to evolve, SZBC-AI4TCM is poised to play a strong role in TCM research and foster its growth in addition to contributing to global health. SZBC-AI4TCM is publicly for access at https://ai.tasly.com/ui/\#/frontend/login.

Indexed as

Alzheimer’s diseaseartificial intelligencebioinformaticsdeep learningtraditional Chinese medicineweb-based computing platform

Identifiers

PMID41333020
PMCPMC12665764

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.