Evidence map›Paper›PMID 40896354›Full record

ArticleIntegrative medicine research2025

A national survey on how to improve the integration of traditional Chinese medicine and artificial intelligence: Attitudes and perceptions from medical staff.

Yinger Gu, Xinyin Hu, Hye Won Lee, Zheng Yao, Tianyi Zhou, Nv Xia, Pingchun Yang, Jinglu Guo, Haifeng Huang, Lisi Wang and 12 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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Ginsenosides fromJournal of ginseng research · 2026
    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

22 authors.

Yinger GuDepartment of Traditional Chinese Medicine, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Xinyin HuDepartment 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.
Zheng YaoDepartment of Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou, China.
Tianyi ZhouDepartment of Traditional Chinese Medicine, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Nv XiaDepartment of Outpatient, Yangming Hospital Affiliated to Ningbo University, Ningbo, China.
Pingchun YangDepartment of Traditional Chinese Medicine, LinCang Maternity and Child Health Care Hospital, LinCang, China.
Jinglu GuoDepartment of Interventional Minimally Invasive Surgery, Taizhou Cancer Hospital, Taizhou, 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.
Wei WangDepartment of Hospital Office, Yuyao Linshan Central Health Center, Ningbo, China.
Cheng WangDepartment of Hospital Office, Shengzhou Changle Town Community Health Center, Shaoxing, China.
Qiaoping ZhaoDepartment of Gynaecology, Shengzhou Hospital of Traditional Chinese Medicine, Shaoxing, China.
Lingling LouDepartment of Physical Examination Center, Shaoxing Hospital of Traditional Chinese Medicine, Shaoxing, China.
Wenjie WuDepartment of Nursing, Yuyao Hospital of Traditional Chinese Medicine, Ningbo, China.
Ke RenDepartment of Hospital Office, Yuyao Mazhu Central Health Center, Ningbo, China.
Guomei YouDepartment of Nursing, Zhejiang Cancer Hospital, Hangzhou, China.
Longlong FanDepartment of Traditional Chinese Medicine, Aksu Prefecture Maternal and Child Health Hospital, Aksu, 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.
Xiaoteng ChenDepartment of Outpatient, Yangming Hospital Affiliated to Ningbo University, Ningbo, 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: With the significant development of artificial intelligence (AI) in recent years, the inheritance and innovation of traditional Chinese medicine (TCM) urgently require the help of AI technology. The present study was to evaluate the attitudes and perceptions of medical staff towards the integration of TCM and AI development. Methods: A cross-sectional national survey was conducted at 13 medical institutions across China. A structured and self-reported questionnaire, consisting of six sections with a total of 14 items, was administered to 1100 medical staff between June 27th and July 11th, 2025. Results: In the process of clinical practice, 62.1 % of medical staff were willing to try TCM diagnosis and treatment services combined with AI. The top three important processes of integration of TCM and AI were medical research, personalized generation of regimen, and intelligent inquiry. The top three concerns about the potential risks associated with the integration of TCM and AI were the misinterpretation of cultural contexts, flexibility in dialectical treatment, and simplification of traditional TCM experience by algorithms. The top three most promising applications were the intelligent syndrome differentiation system (54.6 %), the TCM four diagnostic instruments (49.1 %), and the acupuncture and Tui Na robot (47.8 %). The top three most important factors in the application of AI in TCM were accuracy (78.0 %), convenience of operation (67.5 %), and participation of medical staff (60.9 %). Conclusion: The integration of TCM and AI has a brilliant and promising future, prioritizing diagnostic accuracy while addressing cultural/clinical adaptation challenges in key applications, such as syndrome differentiation systems.

Indexed as

A national surveyArtificial intelligenceMedical staffTraditional Chinese medicine

Identifiers

PMID40896354
PMCPMC12395561

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.