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Updated: Mar 21, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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Deep learning in traditional Chinese medicine.

Li-Ping Liu1, Chen Yang1, Shi-Xin Cen2

  • 1College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.

Journal of Integrative Medicine
|March 19, 2026
PubMed
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Deep learning (DL) offers powerful tools for analyzing traditional Chinese medicine (TCM) data, extracting valuable insights from diverse sources. However, further advancements in DL for TCM require high-quality, comprehensive datasets.

Area of Science:

  • Integrative Medicine
  • Computational Biology
  • Pharmacology

Background:

  • Traditional Chinese Medicine (TCM) relies on ancient knowledge, necessitating modern scientific validation for therapeutic translation.
  • The increasing volume of data from TCM experiments, clinical practice, and literature presents opportunities for advanced analysis.
  • Deep learning (DL) has emerged as a key technology for data mining in various scientific fields, including TCM.

Purpose of the Study:

  • To review recent applications of deep learning (DL) in traditional Chinese medicine (TCM) research.
  • To highlight the potential of DL in extracting meaningful information from complex TCM datasets.
  • To identify challenges and future directions for DL in TCM studies.

Main Methods:

  • Literature review of recent studies employing DL techniques in TCM.
Keywords:
Data fusionDeep learningMedical image processingMedicineNatural language processingTraditional Chinese

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  • Categorization of DL applications across different TCM research areas: medical image processing, medicinal substance investigation, data fusion, and natural language processing.
  • Analysis of the effectiveness and limitations of DL methods in handling TCM data.

    • Main Results:

    • DL has been successfully applied in medical image analysis, drug discovery, data integration, and text mining within TCM.
    • DL methods demonstrate significant potential for uncovering hidden patterns and extracting valuable information from TCM data.
    • The efficacy of DL is contingent upon the availability and quality of TCM-specific datasets.

    Conclusions:

    • Deep learning presents a promising avenue for advancing traditional Chinese medicine research through sophisticated data analysis.
    • High-quality, curated TCM data are crucial for the continued development and successful implementation of DL techniques.
    • Future research should focus on generating robust datasets to fully leverage the capabilities of DL in understanding and applying TCM principles.