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中医四诊人工智能研究的现状与趋势：一项科学计量学研究

Current status and trends of artificial intelligence research on the four traditional Chinese medicine diagnostic methods: a scientometric study.

作者信息

Tian Zhikui, Wang Dongjun, Sun Xuan, Fan Yadong, Guan Yuanyuan, Zhang Naijin, Zhou Mi, Zeng Xianyue, Yuan Yin, Bu Huaien, Wang Hongwu

机构信息

School of Health Sciences and Engineering, Tianjin University of Traditional Chinese Medicine, Tianjin, China.

College of Traditional Chinese Medicine, North China University of Science and Technology, Tangshan, China.

出版信息

Ann Transl Med. 2023 Feb 15;11(3):145. doi: 10.21037/atm-22-6431. Epub 2023 Feb 2.

DOI:10.21037/atm-22-6431
PMID:36846009
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9951008/
Abstract

BACKGROUND: With the development of technology and the renewal of traditional Chinese medicine (TCM) diagnostic equipment, artificial intelligence (AI) has been widely applied in TCM. Numerous articles employing this technology have been published. This study aimed to outline the knowledge and themes trends of the four TCM diagnostic methods to help researchers quickly master the hotspots and trends in this field. Four TCM diagnostic methods is a TCM diagnostic method through inspection, listening, smelling, inquiring and palpation, the purpose of which is to collect the patient's medical history, symptoms and signs. Then, it provides an analytical basis for later disease diagnosis and treatment plans. METHODS: Publications related to AI-based research on the four TCM diagnostic methods were selected from the Web of Science Core Collection, without any restriction on the year of publication. VOSviewer and Citespace were primarily used to create graphical bibliometric maps in this field. RESULTS: China was the most productive country in this field, and published the largest number of related papers, and the Shanghai University of Traditional Chinese Medicine is the dominant research organization. The Chengdu University of Traditional Chinese Medicine had the highest average number of citations. Jinhong Guo was the most influential author and was the most authoritative journal. Six clusters separated by keywords association showed the range of AI-based research on the four TCM diagnostic methods. The hotspots of AI-based research on the four TCM diagnostic methods included the classification and diagnosis of tongue images in patients with diabetes and machine learning for TCM symptom differentiation. CONCLUSIONS: This study demonstrated that AI-based research on the four TCM diagnostic methods is currently in the initial stage of rapid development and has bright prospects. Cross-country and regional cooperation should be strengthened in the future. It is foreseeable that more related research outputs will rely on the interdisciplinarity of TCM and the development of neural networks models.

摘要

背景：随着科技的发展和中医诊断设备的更新，人工智能（AI）已在中医领域得到广泛应用。许多采用该技术的文章已经发表。本研究旨在概述中医四诊法的知识和主题趋势，以帮助研究人员快速掌握该领域的热点和趋势。中医四诊法是一种通过望、闻、问、切来收集患者病史、症状和体征的中医诊断方法，进而为后续疾病诊断和治疗方案提供分析依据。 方法：从Web of Science核心合集中选取与基于人工智能的中医四诊法研究相关的出版物，对出版年份没有任何限制。主要使用VOSviewer和Citespace来创建该领域的图形化文献计量地图。 结果：中国是该领域产出最多的国家，发表的相关论文数量最多，上海中医药大学是主要的研究机构。成都中医药大学的论文平均被引次数最高。郭劲宏是最具影响力的作者，[此处原文缺失最具权威性期刊的具体信息]是最具权威性的期刊。通过关键词关联分离出的六个聚类展示了基于人工智能的中医四诊法研究范围。基于人工智能的中医四诊法研究热点包括糖尿病患者舌象图像的分类与诊断以及用于中医辨证的机器学习。 结论：本研究表明，基于人工智能的中医四诊法研究目前正处于快速发展的初期阶段，前景广阔。未来应加强跨国和跨地区合作。可以预见，更多相关研究成果将依赖于中医的多学科交叉以及神经网络模型的发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/7820ee1a6541/atm-11-03-145-f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/7dd90d10632d/atm-11-03-145-f1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/6a0eab19f655/atm-11-03-145-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/09a556d30f0d/atm-11-03-145-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/8f52a61b96eb/atm-11-03-145-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/4bd3de12c9d6/atm-11-03-145-f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/a3a3da04f1ad/atm-11-03-145-f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/471e2fd46ec1/atm-11-03-145-f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/9c5eb164596b/atm-11-03-145-f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/7820ee1a6541/atm-11-03-145-f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/7dd90d10632d/atm-11-03-145-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/72c9b7f29bfb/atm-11-03-145-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/bb4099c0b2d6/atm-11-03-145-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/2c813bd3b001/atm-11-03-145-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/6a0eab19f655/atm-11-03-145-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/09a556d30f0d/atm-11-03-145-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/8f52a61b96eb/atm-11-03-145-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/4bd3de12c9d6/atm-11-03-145-f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/a3a3da04f1ad/atm-11-03-145-f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/471e2fd46ec1/atm-11-03-145-f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/9c5eb164596b/atm-11-03-145-f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f71a/9951008/7820ee1a6541/atm-11-03-145-f12.jpg

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引用本文的文献

[1]
Bibliometric analysis of the application of artificial intelligence in orthopedic imaging.

Quant Imaging Med Surg. 2025-5-1

[2]
Digital intelligence technology: new quality productivity for precision traditional Chinese medicine.

Front Pharmacol. 2025-4-8

[3]
Exploring the pivotal variables of tongue diagnosis between patients with chronic kidney disease and health participants.

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[4]
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[5]
Intelligent quality control of traditional chinese medical tongue diagnosis images based on deep learning.

Technol Health Care. 2024

本文引用的文献

[1]
The Trends and Hotspots in Premature Ovarian Insufficiency Therapy from 2000 to 2022.

Int J Environ Res Public Health. 2022-9-17

[2]
Global research trends and foci of artificial intelligence-based tumor pathology: a scientometric study.

J Transl Med. 2022-9-6

[3]
A multi-step approach for tongue image classification in patients with diabetes.

Comput Biol Med. 2022-10

[4]
A Traditional Chinese Medicine Syndrome Classification Model Based on Cross-Feature Generation by Convolution Neural Network: Model Development and Validation.

JMIR Med Inform. 2022-4-6

[5]
The Global Research of Artificial Intelligence on Prostate Cancer: A 22-Year Bibliometric Analysis.

Front Oncol. 2022-3-1

[6]
A New Approach of Fatigue Classification Based on Data of Tongue and Pulse With Machine Learning.

Front Physiol. 2022-2-7

[7]
Ensemble Learning-Based Pulse Signal Recognition: Classification Model Development Study.

JMIR Med Inform. 2021-10-21

[8]
A New Method for Syndrome Classification of Non-Small-Cell Lung Cancer Based on Data of Tongue and Pulse with Machine Learning.

Biomed Res Int. 2021

[9]
Artificial Intelligence-Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology.

Biomed Res Int. 2021

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Modeling of diagnosis for metabolic syndrome by integrating symptoms into physiochemical indexes.

Biomed Pharmacother. 2021-5

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