Artificial Intelligence Diagnostic Systems and the Cognitive Model of Traditional Chinese Medicine: Knowledge Transformation under Technological Mediation
DOI:
https://doi.org/10.66581/h51akd76Keywords:
artificial intelligence, Traditional Chinese Medicine diagnosis, human-AI collaboration, knowledge transformationAbstract
The application of artificial intelligence (AI) to Traditional Chinese Medicine (TCM) diagnosis has generated a profound epistemological debate concerning the nature of medical knowledge, the status of clinical experience, and the transformation of diagnostic cognition under technological mediation. Focusing on the four diagnostic methods of inspection, auscultation-olfaction, inquiry, and palpation, this paper examines how AI systems reshape the traditional cognitive model of TCM by transforming sensory, experiential, and holistic forms of judgment into standardized and computable data. Drawing on systems theory, fuzzy logic, tacit knowledge theory, and recent studies of machine learning in TCM diagnosis, the paper argues that AI can improve diagnostic consistency, data integration, and knowledge transmission, but it may also intensify tensions between standardization and individualization, reductionist feature extraction and holistic pattern differentiation, and algorithmic authority and the physician's clinical responsibility. To address these tensions, the paper proposes a human-AI collaborative diagnostic model in which AI serves as an augmentative instrument rather than a replacement for the TCM physician. The proposed model emphasizes TCM-informed algorithm design, multimodal data integration, transparent reasoning, longitudinal personalization, and ethical governance. The study contributes to debates on the modernization of TCM by showing that knowledge transformation should not be understood as the passive digitization of tradition, but as a reciprocal process in which classical diagnostic wisdom and contemporary computational technologies jointly reshape the future of medical cognition.
References
Bertalanffy, L. von. (1968). General system theory: Foundations, development, applications. George Braziller.
Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
Li, W., Ge, X., Liu, S., Xu, L., Zhai, X., & Yu, L. (2024). Opportunities and challenges of traditional Chinese medicine doctors in the era of artificial intelligence. Frontiers in Medicine, 10, Article 1336175. https://doi.org/10.3389/fmed.2023.1336175
Liu, Q., Li, Y., Yang, P., Liu, Q., Wang, C., Chen, K., & Wu, Z. (2023). A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation. Digital Health, 9, 20552076231191044. https://doi.org/10.1177/20552076231191044
Liu, X., Rivera, S. C., Moher, D., Calvert, M. J., & Denniston, A. K. (2020). Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension. Nature Medicine, 26(9), 1364-1374. https://doi.org/10.1038/s41591-020-1034-x
Long, H., Zhu, Y., Jia, L., Gao, B., Liu, J., Liu, L., et al. (2019). An ontological framework for the formalization, organization and usage of TCM-knowledge. BMC Medical Informatics and Decision Making, 19, Article 53. https://doi.org/10.1186/s12911-019-0760-9
Lu, L., Lu, T., Tian, C., & Zhang, X. (2024). AI: Bridging ancient wisdom and modern innovation in traditional Chinese medicine. JMIR Medical Informatics, 12, Article e58491. https://doi.org/10.2196/58491
Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge University Press.
Polanyi, M. (1966). The tacit dimension. University of Chicago Press.
Tian, D., Chen, W., Xu, D., Xu, L., Xu, G., Guo, Y., & Yao, Y. (2024). A review of traditional Chinese medicine diagnosis using machine learning: Inspection, auscultation-olfaction, inquiry, and palpation. Computers in Biology and Medicine, 170, Article 108074. https://doi.org/10.1016/j.compbiomed.2024.108074
Wang, Q. (2009). Constitutional theory of traditional Chinese medicine. People's Medical Publishing House.
Wang, X., Liu, J., Wu, C., Liu, J., Li, Q., Chen, Y., Wang, X., Chen, X., Pang, X., Chang, B., Lin, J., Zhao, S., Li, Z., Deng, Q., Lu, Y., Zhao, D., & Chen, J. (2020). Artificial intelligence in tongue diagnosis: Using deep convolutional neural network for recognizing unhealthy tongue with tooth-mark. Computational and Structural Biotechnology Journal, 18, 973-980. https://doi.org/10.1016/j.csbj.2020.04.002
World Health Organization. (2021). Ethics and governance of artificial intelligence for health: WHO guidance. World Health Organization. https://www.who.int/publications/i/item/9789240029200
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zhang, H., Ni, W., Li, J., & Zhang, J. (2020). Artificial intelligence-based traditional Chinese medicine assistive diagnostic system: Validation study. JMIR Medical Informatics, 8(6), Article e17608. https://doi.org/10.2196/17608
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Abu Sayed Mohammad Mahib (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1.Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY 4.0). This allows others to share the work (copy and redistribute) and adapt the work (remix, transform, and build upon) for any purpose, even commercially, provided the original work is properly cited.
2.Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3.Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
