REVIEW article
Front. Med.
Sec. Healthcare Professions Education
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1633416
Artificial Intelligence in Acupuncture: Bridging Traditional Knowledge and Precision Integrative Medicine
Provisionally accepted
- Liaoning University of Traditional Chinese Medicine, Shenyang, China
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The integration of artificial intelligence (AI) into acupuncture research is accelerating the transformation of this traditional, experience-based practice into a datadriven, precision discipline. This review synthesizes recent advances in AI-enabled outcome prediction techniques, encompassing deep learning, meta-analytic modeling, natural language processing (NLP), computer vision, and neuroimaging-based analysis. For instance, convolutional neural networks (CNNs) have been successfully applied to classify tongue images and detect ZHENG patterns, while transformer-based NLP models enable automated extraction of clinical knowledge from classical texts. These technologies improve diagnostic objectivity, standardize treatment planning, and facilitate individualized care by enabling longitudinal efficacy modeling and real-time monitoring. Despite their potential, current implementations are constrained by limited and heterogeneous datasets, annotation variability, and gaps in clinical validation. We analyze key methodological innovations and challenges, and recommend future directions including the construction of federated multimodal data platforms, development of explainable AI frameworks, and promotion of open science practices.
Keywords: acupuncture outcome prediction, deep learning, artificial intelligence, machine learning, Natural Language Processing, Meta-analysis, precision medicine
Received: 22 May 2025; Accepted: 15 Jul 2025.
Copyright: © 2025 Hou, Dong, Yu, Dai, Lin and Cheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ze-Zhong Cheng, Liaoning University of Traditional Chinese Medicine, Shenyang, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.