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Related Experiment Video

Updated: Jan 11, 2026

Author Spotlight: Development of a Standardized Acupuncture Tool Inspired by Advanced Techniques for Improved Safety and Precision
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Author Spotlight: Development of a Standardized Acupuncture Tool Inspired by Advanced Techniques for Improved Safety and Precision

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Deep learning in acupuncture: A systematic review.

Shu-Cheng Chen1, Yiliang Chen1, Wing-Fai Yeung2

  • 1Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.

Artificial Intelligence in Medicine
|November 13, 2025
PubMed
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This summary is machine-generated.

Deep learning (DL) shows promise in acupuncture, aiding acupoint location, technique analysis, and treatment monitoring. Future work should address data limitations and model interpretability for enhanced clinical applications.

Area of Science:

  • Integrative and Complementary Medicine
  • Artificial Intelligence in Healthcare
  • Biomedical Informatics

Background:

  • Acupuncture is a traditional therapy with growing interest in technological integration.
  • Deep learning (DL) offers advanced computational capabilities for analyzing complex biological and clinical data.
  • The application of DL in acupuncture practice remains an emerging field requiring systematic review.

Purpose of the Study:

  • To systematically review and synthesize existing literature on the use of deep learning (DL) techniques in acupuncture.
  • To identify the primary applications, methodologies, and outcomes of DL in acupuncture research.
  • To highlight current limitations and future directions for DL in acupuncture.

Main Methods:

  • A comprehensive search of multiple electronic databases (Medline, Scopus, etc.) was conducted from 2010 to August 2025.
Keywords:
AcupunctureChinese medicineClinical practiceDeep learningMachine learningSystematic review

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  • Two independent reviewers screened articles and extracted data on study characteristics, DL models, tasks, and outcomes.
  • Qualitative synthesis was employed to summarize findings from 27 included studies.

    • Main Results:

    • DL models were applied to acupoint location detection (15 studies), manipulation analysis (4), disease management (5), and treatment monitoring (3).
    • Studies utilized diverse DL architectures (CNN, RNN, LSTM, BERT, FNN, YOLO variants) on self-built datasets.
    • Key limitations identified were small dataset sizes and model inaccuracies, impacting performance metrics.

    Conclusions:

    • Deep learning models demonstrate significant potential in various acupuncture applications, from diagnostics to treatment monitoring.
    • Emerging DL systems may enhance clinical efficacy and safety, moving beyond initial standardization efforts.
    • Addressing data availability, model interpretability, and establishing AI study appraisal tools are crucial for advancing DL in acupuncture.