A “Four Diagnostic Methods” framework for assisting doctors in traditional Chinese medicine
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A “Four Diagnostic Methods” framework for assisting doctors in traditional Chinese medicine

Bo Gu 1*
1 Anhui University
*Corresponding author: 1345112241@qq.com
Published on 4 August 2025
Journal Cover
AEI Vol.16 Issue 7
ISSN (Print): 2977-3911
ISSN (Online): 2977-3903
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Abstract

Large-Scale Language Models (LLMs) have initiated transformative changes in Traditional Chinese Medicine (TCM), yet existing LLM-based diagnostic approaches face challenges such as prolonged training cycles and high implementation costs due to reliance on medical expertise. To address this, we propose a systematic framework integrating multimodal data and LLM technologies. First, we analyze bottlenecks in traditional diagnosis (e.g., subjectivity) and modernization challenges. The framework leverages open-source foundation models (e.g., Baichuan2, LLaMA) pre-trained on "symptom–syndrome–medication" associations, fine-tuned with clinical data to simulate diagnostic workflows. Key components include: (1) a Data Input Layer capturing tongue image features (via YOLOv5s6/U-Net), speech spectra, BERT-encoded inquiry texts, and pulse waveforms; (2) a Feature Fusion Layer constructing syndrome differentiation vectors through multimodal feature concatenation; and (3) a Prediction & Feedback Layer generating diagnostic probabilities with reinforcement learning based on clinical efficacy. Finally, we discuss critical issues, including risks of physician replacement, professional competence degradation, and liability attribution in diagnostic errors. This framework aims to enhance TCM diagnostic efficiency while ensuring clinical reliability.

Keywords:

TCM four diagnostic methods, large-scale language models, multimodal fusion, clinical diagnostic framework, reinforcement learning

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Gu,B. (2025). A “Four Diagnostic Methods” framework for assisting doctors in traditional Chinese medicine. Advances in Engineering Innovation,16(7),83-91.

References

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[2]. Kang, B., Lee, S., Bae, H., & Kim, C. (2024). Current Status and Direction of Generative Large Language Model Applications in Medicine: Focusing on East Asian Medicine.Journal of Physiology & Pathology in Korean Medicine, 38(2), 49–58. https: //doi.org/10.15188/kjopp.2024.04.38.2.49

[3]. Li, W., Ge, X., Liu, S., Xu, L., Zhai, X., & Yu, L. (2023). Opportunities and challenges of traditional Chinese medicine doctors in the era of artificial intelligence. Frontiers in Medicine,10(1), Article 1336175. https: //doi.org/10.3389/fmed.2023.1336175

[4]. Karatas, M., Zare, Z., & Zheng, Y. (2025). Transforming Preventive Healthcare with Machine Learning Technologies.Journal of Operations Intelligence,3(1): 109-125. https: //doi.org/10.31181/jopi31202538

[5]. Zhu, J., Liu, X., & Gao, P. (2025). Digital intelligence technology: New quality productivity for precision traditional Chinese medicine.Frontiers in Pharmacology, 16(4), Article 1526187. https: //doi.org/10.3389/fphar.2025.1526187

[6]. Cui, J., & Xu, J. (2025). Application status quo and prospects of artificial intelligence and information technology for modernization of four diagnostic methods in traditional Chinese medicine.Shanghai Journal of Traditional Chinese Medicine, 59(1), 7–12. https: //doi.org/10.16305/j.1007-1334.2025.z20240903008

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[8]. Wang, B., Chen, S., Song, J., Huang, D., & Xiao, G. (2024). Recent advances in predicting acute mountain sickness: From multidimensional cohort studies to cutting-edge model applications.Frontiers in Physiology, 15, Article 1397280. https: //doi.org/10.3389/fphys.2024.1397280

[9]. Ren, Y., Luo, X., Wang, Y., Li, H., Zhang, H., Li, Z., Lai, H., Li, X., Ge, L., Estill, J., Zhang, L., Yang, S., Chen, Y., Wen, C., & Bian, Z. (2024). Large language models in traditional Chinese medicine: A scoping review.Journal of Evidence-Based Medicine, 5(1), 57–67. https: //doi.org/10.1111/jebm.12658

[10]. Song, Z., Chen, G., & Chen, C. Y. C. (2024). AI empowering traditional Chinese medicine?Chemical Science,15(41), 16844–16886. https: //doi.org/10.1039/d4sc04107k

[11]. Yang, D., Wei, J., Xiao, D., Wang, S., Wu, T., Li, G., Li, M., Wang, S., Chen, J., Jiang, Y., Xu, Q., Li, K., Zhai, P., & Zhang, L. (2024). PediatricsGPT: Large language models as Chinese medical assistants for pediatric applications.arXiv preprint arXiv: 2405.19266. http: //arxiv.org/abs/2405.19266

[12]. Zhang, H., Wang, X., Meng, Z., Chen, Z., Zhuang, P., Jia, Y., Xu, D., & Guo, W. (2024). Qibo: A large language model for traditional Chinese medicine.Expert Systems with Applications,284, Article 127672. https: //doi.org/10.1016/j.eswa.2025.127672

[13]. Xu, S., Zhou, Y., Liu, Z., Wu, Z., Zhong, T., Zhao, H., Li, Y., Jiang, H., Pan, Y., Chen, J., Lu, J., Zhang, W., Zhang, T., Zhang, L., Zhu, D., Li, X., Liu, W., Li, Q., Sikora, A., … Liu, T. (2024). Towards next-generation medical agent: How o1 is reshaping decision-making in medical scenarios.arXiv preprint arXiv: 2411.14461. http: //arxiv.org/abs/2411.14461

[14]. Yim, W. W., Fu, Y., Ben Abacha, A., & Yetisgen, M. (2024). To err is human, how about medical large language models? Comparing pre-trained language models for medical assessment errors and reliability. In Proceedings of the 2024 Joint International Conference on Computational Linguistics,Language Resources and Evaluation(pp. 16211–16223).

[15]. Liu, L., Yang, X., Lei, J., Liu, X., Shen, Y., Zhang, Z., Wei, P., Gu, J., Chu, Z., Qin, Z., & Ren, K. (2024). A survey on medical large language models: Technology, application, trustworthiness, and future directions.IEEE Journal of Biomedical and Health Informatics, 14(8), 1–26. http: //arxiv.org/abs/2406.03712

[16]. Lucas, H. C., Upperman, J. S., & Robinson, J. R. (2024). A systematic review of large language models and their implications in medical education.Medical Education,58(11), 1276–1285. https: //doi.org/10.1111/medu.15402

[17]. Hamid, R., & Brohi, S. (2024). A review of large language models in healthcare: Taxonomy, threats, vulnerabilities, and framework.Big Data and Cognitive Computing, 8(11), Article 161. https: //doi.org/10.3390/bdcc8110161

[18]. AlSaad, R., Abd-alrazaq, A., Boughorbel, S., Ahmed, A., Renault, M.-A., Damseh, R., & Sheikh, J. (2024). Multimodal large language models in healthcare: Applications, challenges, and future outlook.Journal of Medical Internet Research, 26, Article e59505. https: //doi.org/10.2196/59505

References

[1]. Zhou, X., Dong, X., Li, C., Bai, Y., Xu, Y., Cheung, K. C., See, S., Song, X., Zhang, R., Zhou, X., & Zhang, N. L. (2024). TCM-FTP: Fine-Tuning Large Language Models for Herbal Prescription Prediction. In2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).IEEE. https: //doi.org/10.1109/BIBM62325.2024.10822451

[2]. Kang, B., Lee, S., Bae, H., & Kim, C. (2024). Current Status and Direction of Generative Large Language Model Applications in Medicine: Focusing on East Asian Medicine.Journal of Physiology & Pathology in Korean Medicine, 38(2), 49–58. https: //doi.org/10.15188/kjopp.2024.04.38.2.49

[3]. Li, W., Ge, X., Liu, S., Xu, L., Zhai, X., & Yu, L. (2023). Opportunities and challenges of traditional Chinese medicine doctors in the era of artificial intelligence. Frontiers in Medicine,10(1), Article 1336175. https: //doi.org/10.3389/fmed.2023.1336175

[4]. Karatas, M., Zare, Z., & Zheng, Y. (2025). Transforming Preventive Healthcare with Machine Learning Technologies.Journal of Operations Intelligence,3(1): 109-125. https: //doi.org/10.31181/jopi31202538

[5]. Zhu, J., Liu, X., & Gao, P. (2025). Digital intelligence technology: New quality productivity for precision traditional Chinese medicine.Frontiers in Pharmacology, 16(4), Article 1526187. https: //doi.org/10.3389/fphar.2025.1526187

[6]. Cui, J., & Xu, J. (2025). Application status quo and prospects of artificial intelligence and information technology for modernization of four diagnostic methods in traditional Chinese medicine.Shanghai Journal of Traditional Chinese Medicine, 59(1), 7–12. https: //doi.org/10.16305/j.1007-1334.2025.z20240903008

[7]. Cao, X., Zhang, D., Jin, C., Zhang, Z., & Xue, C. (2025). Multi-Feature Facial Complexion Classification Algorithms Based on CNN.Preprints,Article 2025041664. https: //doi.org/10.20944/preprints202504.1664.v1

[8]. Wang, B., Chen, S., Song, J., Huang, D., & Xiao, G. (2024). Recent advances in predicting acute mountain sickness: From multidimensional cohort studies to cutting-edge model applications.Frontiers in Physiology, 15, Article 1397280. https: //doi.org/10.3389/fphys.2024.1397280

[9]. Ren, Y., Luo, X., Wang, Y., Li, H., Zhang, H., Li, Z., Lai, H., Li, X., Ge, L., Estill, J., Zhang, L., Yang, S., Chen, Y., Wen, C., & Bian, Z. (2024). Large language models in traditional Chinese medicine: A scoping review.Journal of Evidence-Based Medicine, 5(1), 57–67. https: //doi.org/10.1111/jebm.12658

[10]. Song, Z., Chen, G., & Chen, C. Y. C. (2024). AI empowering traditional Chinese medicine?Chemical Science,15(41), 16844–16886. https: //doi.org/10.1039/d4sc04107k

[11]. Yang, D., Wei, J., Xiao, D., Wang, S., Wu, T., Li, G., Li, M., Wang, S., Chen, J., Jiang, Y., Xu, Q., Li, K., Zhai, P., & Zhang, L. (2024). PediatricsGPT: Large language models as Chinese medical assistants for pediatric applications.arXiv preprint arXiv: 2405.19266. http: //arxiv.org/abs/2405.19266

[12]. Zhang, H., Wang, X., Meng, Z., Chen, Z., Zhuang, P., Jia, Y., Xu, D., & Guo, W. (2024). Qibo: A large language model for traditional Chinese medicine.Expert Systems with Applications,284, Article 127672. https: //doi.org/10.1016/j.eswa.2025.127672

[13]. Xu, S., Zhou, Y., Liu, Z., Wu, Z., Zhong, T., Zhao, H., Li, Y., Jiang, H., Pan, Y., Chen, J., Lu, J., Zhang, W., Zhang, T., Zhang, L., Zhu, D., Li, X., Liu, W., Li, Q., Sikora, A., … Liu, T. (2024). Towards next-generation medical agent: How o1 is reshaping decision-making in medical scenarios.arXiv preprint arXiv: 2411.14461. http: //arxiv.org/abs/2411.14461

[14]. Yim, W. W., Fu, Y., Ben Abacha, A., & Yetisgen, M. (2024). To err is human, how about medical large language models? Comparing pre-trained language models for medical assessment errors and reliability. In Proceedings of the 2024 Joint International Conference on Computational Linguistics,Language Resources and Evaluation(pp. 16211–16223).

[15]. Liu, L., Yang, X., Lei, J., Liu, X., Shen, Y., Zhang, Z., Wei, P., Gu, J., Chu, Z., Qin, Z., & Ren, K. (2024). A survey on medical large language models: Technology, application, trustworthiness, and future directions.IEEE Journal of Biomedical and Health Informatics, 14(8), 1–26. http: //arxiv.org/abs/2406.03712

[16]. Lucas, H. C., Upperman, J. S., & Robinson, J. R. (2024). A systematic review of large language models and their implications in medical education.Medical Education,58(11), 1276–1285. https: //doi.org/10.1111/medu.15402

[17]. Hamid, R., & Brohi, S. (2024). A review of large language models in healthcare: Taxonomy, threats, vulnerabilities, and framework.Big Data and Cognitive Computing, 8(11), Article 161. https: //doi.org/10.3390/bdcc8110161

[18]. AlSaad, R., Abd-alrazaq, A., Boughorbel, S., Ahmed, A., Renault, M.-A., Damseh, R., & Sheikh, J. (2024). Multimodal large language models in healthcare: Applications, challenges, and future outlook.Journal of Medical Internet Research, 26, Article e59505. https: //doi.org/10.2196/59505

Cite this article

Gu,B. (2025). A “Four Diagnostic Methods” framework for assisting doctors in traditional Chinese medicine. Advances in Engineering Innovation,16(7),83-91.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Journal: Advances in Engineering Innovation

Volume number: Vol.16
Issue number: Issue 7
ISSN: 2977-3903(Print) / 2977-3911(Online)

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