Evidence mapPaperPMID 40896351Full record

ArticleIntegrative medicine research2025

A national survey on the integration of traditional Chinese medicine and artificial intelligence: attitudes and perceptions from the individuals with health needs.

Xinyin Hu, Yinger Gu, Hye Won Lee, Xiaoteng Chen, Ying Li, Xinyue Li, Qiaoping Zhao, Wei Wang, Haifeng Huang, Lisi Wang and 13 more

Abstract read
In one paragraph

Article in Integrative medicine research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Ginsenosides fromJournal of ginseng research · 2026
    Review
  4. Article
  5. Article
  6. Article
  7. Article
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

23 authors.

Xinyin HuDepartment of Traditional Chinese Medicine, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Yinger GuDepartment of Traditional Chinese Medicine, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Hye Won LeeKM Convergence Research Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea.
Xiaoteng ChenDepartment of Outpatient, Yangming Hospital Affiliated to Ningbo University, Ningbo, China.
Ying LiDepartment of Traditional Chinese Medicine, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Xinyue LiDepartment of Traditional Chinese Medicine, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Qiaoping ZhaoDepartment of Gynaecology, Shengzhou Hospital of Traditional Chinese Medicine, Shaoxing, China.
Wei WangDepartment of Hospital Office, Yuyao Linshan Central Health Center, Ningbo, China.
Haifeng HuangDepartment of Traditional Chinese Medicine, Dongxin subdistrict community health service, Gongshu district, Hangzhou, China.
Lisi WangDepartment of Traditional Chinese Medicine Internal Medicine, Wenhui subdistrict community health service, Gongshu district, Hangzhou, China.
Nv XiaDepartment of Outpatient, Yangming Hospital Affiliated to Ningbo University, Ningbo, China.
Wenjie WuDepartment of Nursing, Yuyao Hospital of Traditional Chinese Medicine, Ningbo, China.
Lingling LouDepartment of Physical Examination Center, Shaoxing Hospital of Traditional Chinese Medicine, Shaoxing, China.
Pingchun YangDepartment of Traditional Chinese Medicine, Lincang Maternity and Child Health Care Hospital, Lincang, China.
Ke RenDepartment of Hospital Office, Yuyao Mazhu Central Health Center, Ningbo, China.
Jinglu GuoDepartment of Interventional Minimally Invasive Surgery, Taizhou Cancer Hospital, Taizhou, China.
Cheng WangDepartment of Hospital Office, Shengzhou Changle Town Community Health Center, Shaoxing, China.
Longlong FanDepartment of Traditional Chinese Medicine, Aksu Prefecture Maternal and Child Health Hospital, Aksu, China.
Zheng YaoDepartment of Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou, China.
Guomei YouDepartment of Nursing, Zhejiang Cancer Hospital, Hangzhou, China.
Jue ZhouSchool of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou, China.
Fangfang WangDepartment of Traditional Chinese Medicine, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Fan QuDepartment of Traditional Chinese Medicine, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Traditional Chinese medicine (TCM) plays an indispensable role in the healthcare system. Artificial intelligence (AI) opens a new pathway for TCM modernization, while also addressing critical healthcare challenges. The present national survey was conducted to assess the attitudes and perceptions of individuals with health needs regarding the integration of TCM with AI. Methods: A cross-sectional national survey was conducted at 13 medical institutions across China. A structured, self-reported questionnaire was administered to 2587 individuals with health needs, including patients seeking TCM/Western medical treatment and individuals undergoing routine physical examinations, between June 27th and July 11th, 2025. Results: A total of 1641 (63.4 %) respondents were familiar with the TCM-AI equipment, and 61.7 % respondents were willing to try TCM diagnosis and treatment services combined with AI. 43.5 % respondents trusted the diagnosis results provided by the TCM-AI equipment. In the subgroup analysis, respondents aged 18-34, with a bachelor's degree or associate's degree as their educational background, and working as employees of state organs, showed greater acceptance and trust towards the integration of TCM and AI ( Conclusion: The integration of TCM and AI demonstrates promising acceptance among health-seeking individuals in China, with younger and educated populations who have health demands for TCM showing particularly high trust, and intelligent syndrome differentiation systems highlight a clear pathway for AI to modernize TCM practice by augmenting diagnostic accuracy and treatment personalization.

Indexed as

A national surveyArtificial intelligenceIndividuals with health needsTraditional Chinese medicine

Identifiers

PMID40896351
PMCPMC12395560

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.