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数字智能技术:精准中医的新型质量生产力。

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

作者信息

Zhu Junqing, Liu Xiaonan, Gao Peng

机构信息

Shandong Key Laboratory of Digital Traditional Chinese Medicine, Institute of Pharmaceutical Research, Shandong University of Traditional Chinese Medicine, Jinan, China.

出版信息

Front Pharmacol. 2025 Apr 8;16:1526187. doi: 10.3389/fphar.2025.1526187. eCollection 2025.


DOI:10.3389/fphar.2025.1526187
PMID:40264673
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12012302/
Abstract

Traditional Chinese medicine is a complex medical system characterized by multiple metabolites, targets, and pathways, known for its low drug resistance and significant efficacy. However, challenges persist within Traditional Chinese medicine, including difficulties in assessing the quality of Botanical drugs, reliance on experiential knowledge for disease diagnosis and treatment, and a lack of clarity regarding the pharmacological mechanisms of Traditional Chinese medicine. The advancement of digital intelligence technology is driving a shift towards precision medicine within the Traditional Chinese medicine model. This transition propels Traditional Chinese medicine into an era of precision, intelligence, and digitalization. This paper introduces standard digital intelligence technologies and explores the application of digital intelligence technologies in quality control and evaluation of Traditional Chinese medicine, studies the research status of digital intelligence technologies in assisting diagnosis, treatment and prevention of diseases, and further promotes the application and development of digital intelligence technologies in the field of Traditional Chinese medicine.

摘要

传统中医是一个复杂的医学体系,其特点是具有多种代谢产物、靶点和途径,以低耐药性和显著疗效而闻名。然而,传统中医仍然面临挑战,包括难以评估植物药的质量、疾病诊断和治疗依赖经验知识,以及传统中医药理机制尚不清楚。数字智能技术的进步正在推动传统中医模式向精准医学转变。这一转变将传统中医带入了一个精准、智能和数字化的时代。本文介绍了标准数字智能技术,探讨了数字智能技术在中药质量控制和评价中的应用,研究了数字智能技术在辅助疾病诊断、治疗和预防方面的研究现状,并进一步推动数字智能技术在传统中医领域的应用和发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/710c/12012302/bfd87a6aad5f/fphar-16-1526187-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/710c/12012302/c133fa541648/fphar-16-1526187-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/710c/12012302/a509fbebceb7/fphar-16-1526187-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/710c/12012302/c04ad98aa46c/fphar-16-1526187-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/710c/12012302/f1d73c9c534e/fphar-16-1526187-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/710c/12012302/bfd87a6aad5f/fphar-16-1526187-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/710c/12012302/c133fa541648/fphar-16-1526187-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/710c/12012302/a509fbebceb7/fphar-16-1526187-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/710c/12012302/c04ad98aa46c/fphar-16-1526187-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/710c/12012302/f1d73c9c534e/fphar-16-1526187-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/710c/12012302/bfd87a6aad5f/fphar-16-1526187-g005.jpg

相似文献

[1]
Digital intelligence technology: new quality productivity for precision traditional Chinese medicine.

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[2]
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[3]
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[4]
[Current situation and development trend of digital traditional Chinese medicine pharmacy].

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[5]
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[7]
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[8]
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[Overview and prospects of traditional Chinese medicine blending technology oriented by quality consistency].

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引用本文的文献

[1]
A national survey on the integration of traditional Chinese medicine and artificial intelligence: attitudes and perceptions from the individuals with health needs.

Integr Med Res. 2025-12

[2]
Chinese herbal medicine for chronic pain: a bibliometric analysis based on integrated databases (2011-2024).

Front Med (Lausanne). 2025-8-8

本文引用的文献

[1]
PDGCL-DTI: Parallel Dual-channel Graph Contrastive Learning for Drug-Target Binding Prediction in Heterogeneous Networks.

IEEE J Biomed Health Inform. 2024-12-18

[2]
[Current situation and development trend of digital traditional Chinese medicine pharmacy].

Zhongguo Zhong Yao Za Zhi. 2024-1

[3]
A review of traditional Chinese medicine diagnosis using machine learning: Inspection, auscultation-olfaction, inquiry, and palpation.

Comput Biol Med. 2024-3

[4]
Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning.

Integr Med Res. 2024-3

[5]
Recent trends of machine learning applied to multi-source data of medicinal plants.

J Pharm Anal. 2023-12

[6]
Metabolic syndrome prediction model using Bayesian optimization and XGBoost based on traditional Chinese medicine features.

Heliyon. 2023-11-30

[7]
Constructing a screening model to obtain the functional herbs for the treatment of active ulcerative colitis based on herb-compound-target network and immuno-infiltration analysis.

Naunyn Schmiedebergs Arch Pharmacol. 2024-7

[8]
Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review.

Front Pharmacol. 2023-11-14

[9]
Traditional Chinese Medicine studies for Alzheimer's disease via network pharmacology based on entropy and random walk.

PLoS One. 2023

[10]
Rapid Indentification of Auramine O Dyeing Adulteration in , and by SERS Raman Spectroscopy Combined with SSA-BP Neural Networks Model.

Foods. 2023-11-14

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