A Review of the Current State of Multimodal Data Annotation in Traditional Chinese Medicine

Du Yutong; Yao Yuan; Zhou Hongwei

doi:doi:10.11648/j.ajhr.20251306.17

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A Review of the Current State of Multimodal Data Annotation in Traditional Chinese Medicine

Du Yutong

, Yao Yuan

, Zhou Hongwei^*

Published in American Journal of Health Research (Volume 13, Issue 6)

Received: 28 October 2025 Accepted: 12 November 2025 Published: 11 December 2025

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Abstract

Integrating artificial intelligence with traditional Chinese medicine (TCM) is a vital step towards the modernization and intelligent development of TCM. The aim of this article is to explore the current state of research and development in the annotation of multimodal data for TCM. First, the paper elucidates the background and core significance of multimodal data annotation. Then, it provides a systematic analysis of the various sources of TCM data, including textual materials such as classical texts and modern medical case records; imaging data such as facial, tongue, and ocular diagnoses; and signal data such as pulse and auscultation diagnoses. It also covers the technical characteristics of the annotation of these data types. Subsequently, the article explores detailed annotation methodologies tailored to various application scenarios. These include knowledge representation for classical Chinese medicine texts, in-depth annotation to capture the expertise of renowned traditional Chinese medicine practitioners, and standardized annotation for modern clinical information systems. Finally, this paper focuses on exploring the cross-disciplinary integration of global workspace theory (GWT) in cognitive neuroscience with multi-granularity computing and multimodal annotation in computer science. GWT offers an approach to understanding how diverse diagnostic information is synthesized into a coherent awareness, mirroring the process of TCM pattern differentiation. Multi-granularity computation is a methodological approach to processing this information through attention mechanisms at different levels. By integrating these advanced concepts with a practical framework for multimodal annotation, this paper aims to provide theoretical support and methodological insights for constructing intelligent diagnostic and therapeutic systems tailored to the cognitive characteristics of TCM.

Published in	American Journal of Health Research (Volume 13, Issue 6)
DOI	10.11648/j.ajhr.20251306.17
Page(s)	343-351
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Traditional Chinese Medicine, Multimodal Fusion, Data Annotation, Review

References

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Yutong, D., Yuan, Y., Hongwei, Z. (2025). A Review of the Current State of Multimodal Data Annotation in Traditional Chinese Medicine. American Journal of Health Research, 13(6), 343-351. https://doi.org/10.11648/j.ajhr.20251306.17

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Yutong, D.; Yuan, Y.; Hongwei, Z. A Review of the Current State of Multimodal Data Annotation in Traditional Chinese Medicine. Am. J. Health Res. 2025, 13(6), 343-351. doi: 10.11648/j.ajhr.20251306.17

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AMA Style

Yutong D, Yuan Y, Hongwei Z. A Review of the Current State of Multimodal Data Annotation in Traditional Chinese Medicine. Am J Health Res. 2025;13(6):343-351. doi: 10.11648/j.ajhr.20251306.17

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@article{10.11648/j.ajhr.20251306.17,
  author = {Du Yutong and Yao Yuan and Zhou Hongwei},
  title = {A Review of the Current State of Multimodal Data Annotation in Traditional Chinese Medicine},
  journal = {American Journal of Health Research},
  volume = {13},
  number = {6},
  pages = {343-351},
  doi = {10.11648/j.ajhr.20251306.17},
  url = {https://doi.org/10.11648/j.ajhr.20251306.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajhr.20251306.17},
  abstract = {Integrating artificial intelligence with traditional Chinese medicine (TCM) is a vital step towards the modernization and intelligent development of TCM. The aim of this article is to explore the current state of research and development in the annotation of multimodal data for TCM. First, the paper elucidates the background and core significance of multimodal data annotation. Then, it provides a systematic analysis of the various sources of TCM data, including textual materials such as classical texts and modern medical case records; imaging data such as facial, tongue, and ocular diagnoses; and signal data such as pulse and auscultation diagnoses. It also covers the technical characteristics of the annotation of these data types. Subsequently, the article explores detailed annotation methodologies tailored to various application scenarios. These include knowledge representation for classical Chinese medicine texts, in-depth annotation to capture the expertise of renowned traditional Chinese medicine practitioners, and standardized annotation for modern clinical information systems. Finally, this paper focuses on exploring the cross-disciplinary integration of global workspace theory (GWT) in cognitive neuroscience with multi-granularity computing and multimodal annotation in computer science. GWT offers an approach to understanding how diverse diagnostic information is synthesized into a coherent awareness, mirroring the process of TCM pattern differentiation. Multi-granularity computation is a methodological approach to processing this information through attention mechanisms at different levels. By integrating these advanced concepts with a practical framework for multimodal annotation, this paper aims to provide theoretical support and methodological insights for constructing intelligent diagnostic and therapeutic systems tailored to the cognitive characteristics of TCM.},
 year = {2025}
}

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TY  - JOUR
T1  - A Review of the Current State of Multimodal Data Annotation in Traditional Chinese Medicine
AU  - Du Yutong
AU  - Yao Yuan
AU  - Zhou Hongwei
Y1  - 2025/12/11
PY  - 2025
N1  - https://doi.org/10.11648/j.ajhr.20251306.17
DO  - 10.11648/j.ajhr.20251306.17
T2  - American Journal of Health Research
JF  - American Journal of Health Research
JO  - American Journal of Health Research
SP  - 343
EP  - 351
PB  - Science Publishing Group
SN  - 2330-8796
UR  - https://doi.org/10.11648/j.ajhr.20251306.17
AB  - Integrating artificial intelligence with traditional Chinese medicine (TCM) is a vital step towards the modernization and intelligent development of TCM. The aim of this article is to explore the current state of research and development in the annotation of multimodal data for TCM. First, the paper elucidates the background and core significance of multimodal data annotation. Then, it provides a systematic analysis of the various sources of TCM data, including textual materials such as classical texts and modern medical case records; imaging data such as facial, tongue, and ocular diagnoses; and signal data such as pulse and auscultation diagnoses. It also covers the technical characteristics of the annotation of these data types. Subsequently, the article explores detailed annotation methodologies tailored to various application scenarios. These include knowledge representation for classical Chinese medicine texts, in-depth annotation to capture the expertise of renowned traditional Chinese medicine practitioners, and standardized annotation for modern clinical information systems. Finally, this paper focuses on exploring the cross-disciplinary integration of global workspace theory (GWT) in cognitive neuroscience with multi-granularity computing and multimodal annotation in computer science. GWT offers an approach to understanding how diverse diagnostic information is synthesized into a coherent awareness, mirroring the process of TCM pattern differentiation. Multi-granularity computation is a methodological approach to processing this information through attention mechanisms at different levels. By integrating these advanced concepts with a practical framework for multimodal annotation, this paper aims to provide theoretical support and methodological insights for constructing intelligent diagnostic and therapeutic systems tailored to the cognitive characteristics of TCM.
VL  - 13
IS  - 6
ER  -

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Author Information

Du Yutong

Chinese Medicine Data Center, China Academy of Chinese Medical Sciences, Beijing, China

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http://orcid.org/0009-0008-3904-3557
Yao Yuan

Chinese Medicine Data Center, China Academy of Chinese Medical Sciences, Beijing, China

Contact Email

http://orcid.org/0009-0005-6234-951X
Zhou Hongwei

Chinese Medicine Data Center, China Academy of Chinese Medical Sciences, Beijing, China

Contact Email

http://orcid.org/0009-0006-0683-2061

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APA Style

Yutong, D., Yuan, Y., Hongwei, Z. (2025). A Review of the Current State of Multimodal Data Annotation in Traditional Chinese Medicine. American Journal of Health Research, 13(6), 343-351. https://doi.org/10.11648/j.ajhr.20251306.17

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ACS Style

Yutong, D.; Yuan, Y.; Hongwei, Z. A Review of the Current State of Multimodal Data Annotation in Traditional Chinese Medicine. Am. J. Health Res. 2025, 13(6), 343-351. doi: 10.11648/j.ajhr.20251306.17

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AMA Style

Yutong D, Yuan Y, Hongwei Z. A Review of the Current State of Multimodal Data Annotation in Traditional Chinese Medicine. Am J Health Res. 2025;13(6):343-351. doi: 10.11648/j.ajhr.20251306.17

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@article{10.11648/j.ajhr.20251306.17,
  author = {Du Yutong and Yao Yuan and Zhou Hongwei},
  title = {A Review of the Current State of Multimodal Data Annotation in Traditional Chinese Medicine},
  journal = {American Journal of Health Research},
  volume = {13},
  number = {6},
  pages = {343-351},
  doi = {10.11648/j.ajhr.20251306.17},
  url = {https://doi.org/10.11648/j.ajhr.20251306.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajhr.20251306.17},
  abstract = {Integrating artificial intelligence with traditional Chinese medicine (TCM) is a vital step towards the modernization and intelligent development of TCM. The aim of this article is to explore the current state of research and development in the annotation of multimodal data for TCM. First, the paper elucidates the background and core significance of multimodal data annotation. Then, it provides a systematic analysis of the various sources of TCM data, including textual materials such as classical texts and modern medical case records; imaging data such as facial, tongue, and ocular diagnoses; and signal data such as pulse and auscultation diagnoses. It also covers the technical characteristics of the annotation of these data types. Subsequently, the article explores detailed annotation methodologies tailored to various application scenarios. These include knowledge representation for classical Chinese medicine texts, in-depth annotation to capture the expertise of renowned traditional Chinese medicine practitioners, and standardized annotation for modern clinical information systems. Finally, this paper focuses on exploring the cross-disciplinary integration of global workspace theory (GWT) in cognitive neuroscience with multi-granularity computing and multimodal annotation in computer science. GWT offers an approach to understanding how diverse diagnostic information is synthesized into a coherent awareness, mirroring the process of TCM pattern differentiation. Multi-granularity computation is a methodological approach to processing this information through attention mechanisms at different levels. By integrating these advanced concepts with a practical framework for multimodal annotation, this paper aims to provide theoretical support and methodological insights for constructing intelligent diagnostic and therapeutic systems tailored to the cognitive characteristics of TCM.},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - A Review of the Current State of Multimodal Data Annotation in Traditional Chinese Medicine
AU  - Du Yutong
AU  - Yao Yuan
AU  - Zhou Hongwei
Y1  - 2025/12/11
PY  - 2025
N1  - https://doi.org/10.11648/j.ajhr.20251306.17
DO  - 10.11648/j.ajhr.20251306.17
T2  - American Journal of Health Research
JF  - American Journal of Health Research
JO  - American Journal of Health Research
SP  - 343
EP  - 351
PB  - Science Publishing Group
SN  - 2330-8796
UR  - https://doi.org/10.11648/j.ajhr.20251306.17
AB  - Integrating artificial intelligence with traditional Chinese medicine (TCM) is a vital step towards the modernization and intelligent development of TCM. The aim of this article is to explore the current state of research and development in the annotation of multimodal data for TCM. First, the paper elucidates the background and core significance of multimodal data annotation. Then, it provides a systematic analysis of the various sources of TCM data, including textual materials such as classical texts and modern medical case records; imaging data such as facial, tongue, and ocular diagnoses; and signal data such as pulse and auscultation diagnoses. It also covers the technical characteristics of the annotation of these data types. Subsequently, the article explores detailed annotation methodologies tailored to various application scenarios. These include knowledge representation for classical Chinese medicine texts, in-depth annotation to capture the expertise of renowned traditional Chinese medicine practitioners, and standardized annotation for modern clinical information systems. Finally, this paper focuses on exploring the cross-disciplinary integration of global workspace theory (GWT) in cognitive neuroscience with multi-granularity computing and multimodal annotation in computer science. GWT offers an approach to understanding how diverse diagnostic information is synthesized into a coherent awareness, mirroring the process of TCM pattern differentiation. Multi-granularity computation is a methodological approach to processing this information through attention mechanisms at different levels. By integrating these advanced concepts with a practical framework for multimodal annotation, this paper aims to provide theoretical support and methodological insights for constructing intelligent diagnostic and therapeutic systems tailored to the cognitive characteristics of TCM.
VL  - 13
IS  - 6
ER  -

Copy | Download