Evidence map›Paper›PMID 40823261›Full record

ReviewJournal of pharmaceutical analysis2025

The integration of machine learning into traditional Chinese medicine.

Yanfeng Hong, Sisi Zhu, Yuhong Liu, Chao Tian, Hongquan Xu, Gongxing Chen, Lin Tao, Tian Xie

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of pharmaceutical analysis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Review
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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

8 authors.

Yanfeng HongSchool of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
Sisi ZhuSchool of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
Yuhong LiuSchool of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
Chao TianSchool of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
Hongquan XuSchool of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
Gongxing ChenSchool of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
Lin TaoSchool of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.
Tian XieSchool of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional Chinese medicine (TCM) is an ancient medical system distinctive and effective in treating cancer, depression, coronavirus disease 2019 (COVID-19), and other diseases. However, the relatively abstract diagnostic methods of TCM lack objective measurement, and the complex mechanisms of action are difficult to comprehend, which hinders the application and internationalization of TCM. Recently, while breakthroughs have been made in utilizing methods such as network pharmacology and virtual screening for TCM research, the rise of machine learning (ML) has significantly enhanced their integration with TCM. This article introduces representative methodological cases in quality control, mechanism research, diagnosis, and treatment processes of TCM, revealing the potential applications of ML technology in TCM. Furthermore, the challenges faced by ML in TCM applications are summarized, and future directions are discussed.

Indexed as

Drug developmentMachine learningPrecision medicineQuality controlTraditional Chinese medicine

Identifiers

PMID40823261
PMCPMC12356308

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.