Evidence map›Paper›PMID 33763147›Full record

ReviewEvidence-based complementary and alternative medicine : eCAM2021

Development and Application of Artificial Intelligence in Auxiliary TCM Diagnosis.

Chuwen Feng, Yuming Shao, Bing Wang, Yuanyuan Qu, Qingyong Wang, Yang Li, Tiansong Yang

Abstract readReview
In one paragraph

Review in Evidence-based complementary and alternative medicine : eCAM, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. 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

7 authors.

Chuwen FengHeilongjiang University of Chinese Medicine, Harbin 150040, Heilongjiang, China.ORCID https://orcid.org/0000-0002-1632-6588
Yuming ShaoHeilongjiang University of Chinese Medicine, Harbin 150040, Heilongjiang, China.ORCID https://orcid.org/0000-0003-1709-3680
Bing WangHeilongjiang University of Chinese Medicine, Harbin 150040, Heilongjiang, China.ORCID https://orcid.org/0000-0002-6558-8971
Yuanyuan QuHeilongjiang University of Chinese Medicine, Harbin 150040, Heilongjiang, China.ORCID https://orcid.org/0000-0002-6900-6510
Qingyong WangHeilongjiang University of Chinese Medicine, Harbin 150040, Heilongjiang, China.ORCID https://orcid.org/0000-0003-2376-5105
Yang LiHeilongjiang University of Chinese Medicine, Harbin 150040, Heilongjiang, China.ORCID https://orcid.org/0000-0003-0954-2431
Tiansong YangHeilongjiang University of Chinese Medicine, Harbin 150040, Heilongjiang, China.ORCID https://orcid.org/0000-0002-4008-702X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As an emerging comprehensive discipline, artificial intelligence (AI) has been widely applied in various fields, including traditional Chinese medicine (TCM), a treasure of the Chinese nation. Realizing the organic combination of AI and TCM can promote the inheritance and development of TCM. The paper summarizes the development and application of AI in auxiliary TCM diagnosis, analyzes the bottleneck of artificial intelligence in the field of auxiliary TCM diagnosis at present, and proposes a possible future direction of its development.

Identifiers

PMID33763147
PMCPMC7955861

What OpenQuestion holds

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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.