Artificial intelligence for Traditional Chinese Medicine classic formula development: a technology readiness assessment using metabolic syndrome as a case study
5 affiliations
- 1State Key Laboratory for the Modernization of Classical and Famous Prescriptions of Chinese Medicine
- 2Jiangxi Key Laboratory of Molecular Medicine, The Second Affiliated Hospital of Nanchang University
- 3Jiangxi Science and Technology Infrastructure Center, Jiangxi Computing Center
- 4The Affiliated Hospital of Jiangxi University of Chinese Medicine
- 5Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University
Abstract
Traditional Chinese medicine (TCM) formulas, developed through millennia of clinical practice, represent a rich legacy of approximately 100,000 documented prescriptions (99,584 named formula entries in the second edition of the Dictionary of Traditional Chinese Medicine Formulas), yet on average fewer than ten new TCM products were approved annually by China’s NMPA from 2013 to 2024 (77 approvals in total). The experience-based screening paradigm is misaligned with the precision medicine framework demanded by metabolic syndrome (MetS), a multi-target disorder affecting an estimated 1.54 billion adults (95% CrI 1.35–1.76) in 2023. Although artificial intelligence (AI)—encompassing knowledge graphs (KGs), large language models (LLMs), and high-throughput experimentation—has been proposed as a solution, its actual readiness for TCM development remains unclear. This review critically examines KG, LLM, and high-throughput validation technologies enabling precision phytomedicine for MetS, providing a realistic roadmap for AI-driven TCM research and development. The three technology streams exhibit asymmetric readiness. At the stream level, knowledge-graph approaches span Technology Readiness Level (TRL) 3–6, large language model approaches span TRL 1–5, and high-throughput validation platforms span TRL 3–5. LLM development is limited by three TCM-specific hallucination types that lack evaluation benchmarks. KG entity recognition is the most mature component, yet reasoning is hindered by the absence of negative data, missing dose–time dimensions, and unencoded monarch–minister–assistant–envoy hierarchies. High-throughput platforms are technically feasible but remain mismatched to MetS functional endpoints—tumor-derived organoid paradigms show limited direct transferability and would require substantial adaptation. Precision phytomedicine must follow a “foundations before algorithms” sequence: institutionalize negative data, build TCM-Hallu benchmarks, establish functional-endpoint evaluation, and develop AI-assisted regulatory review. Algorithmic advances will remain in an incomplete information space until knowledge infrastructure gaps are closed.
Related
- Deep Reinforcement Learning-based combat recognition of traditional Chinese Sanda under artificial intelligence technology
- Development and validation of an artificial intelligence model for basal cell carcinoma detection and section completeness assessment in Mohs micrographic surgery in an Asian cohort
- Revolutionizing transdermal drug delivery evaluation: the evolution from traditional models to skin organoids and the integration of artificial intelligence
- Strategies for integrating artificial intelligence and cognitive assessment to predict disability progression in relapsing-remitting multiple sclerosis: A model development study
- The emerging role of artificial intelligence in health technology incubators: A Narrative Review
BibTeX
@article{huang2026artificial,
title = {{Artificial intelligence for Traditional Chinese Medicine classic formula development: a technology readiness assessment using metabolic syndrome as a case study}},
author = {Zihao Huang and Zhenhong Jiang and Shaowen Hu and Ning Ye and Xingteng Liu and Xinyi Zhan and Zhenhui Wu and Hao Chen and Zihao Zhao and Xinyu Li and Zhenzhong Zang and Chuanyun Xiao and Xiangmeng Qu and Langxin Huang and Haowei Peng and Xiang Zhou and Ming Yang and Huanhua Xu},
journal = {Frontiers in Pharmacology},
year = {2026},
month = oct,
doi = {10.3389/fphar.2026.1970742},
url = {https://www.frontiersin.org/articles/10.3389/fphar.2026.1970742/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial intelligence for Traditional Chinese Medicine classic formula development: a technology readiness assessment using metabolic syndrome as a case study
AU - Zihao Huang
AU - Zhenhong Jiang
AU - Shaowen Hu
AU - Ning Ye
AU - Xingteng Liu
AU - Xinyi Zhan
AU - Zhenhui Wu
AU - Hao Chen
AU - Zihao Zhao
AU - Xinyu Li
AU - Zhenzhong Zang
AU - Chuanyun Xiao
AU - Xiangmeng Qu
AU - Langxin Huang
AU - Haowei Peng
AU - Xiang Zhou
AU - Ming Yang
AU - Huanhua Xu
PY - 2026
DA - 2026/10/28
JO - Frontiers in Pharmacology
DO - 10.3389/fphar.2026.1970742
UR - https://www.frontiersin.org/articles/10.3389/fphar.2026.1970742/full
AB - Traditional Chinese medicine (TCM) formulas, developed through millennia of clinical practice, represent a rich legacy of approximately 100,000 documented prescriptions (99,584 named formula entries in the second edition of the Dictionary of Traditional Chinese Medicine Formulas), yet on average fewer than ten new TCM products were approved annually by China’s NMPA from 2013 to 2024 (77 approvals in total). The experience-based screening paradigm is misaligned with the precision medicine framework demanded by metabolic syndrome (MetS), a multi-target disorder affecting an estimated 1.54 billion adults (95% CrI 1.35–1.76) in 2023. Although artificial intelligence (AI)—encompassing knowledge graphs (KGs), large language models (LLMs), and high-throughput experimentation—has been proposed as a solution, its actual readiness for TCM development remains unclear. This review critically examines KG, LLM, and high-throughput validation technologies enabling precision phytomedicine for MetS, providing a realistic roadmap for AI-driven TCM research and development. The three technology streams exhibit asymmetric readiness. At the stream level, knowledge-graph approaches span Technology Readiness Level (TRL) 3–6, large language model approaches span TRL 1–5, and high-throughput validation platforms span TRL 3–5. LLM development is limited by three TCM-specific hallucination types that lack evaluation benchmarks. KG entity recognition is the most mature component, yet reasoning is hindered by the absence of negative data, missing dose–time dimensions, and unencoded monarch–minister–assistant–envoy hierarchies. High-throughput platforms are technically feasible but remain mismatched to MetS functional endpoints—tumor-derived organoid paradigms show limited direct transferability and would require substantial adaptation. Precision phytomedicine must follow a “foundations before algorithms” sequence: institutionalize negative data, build TCM-Hallu benchmarks, establish functional-endpoint evaluation, and develop AI-assisted regulatory review. Algorithmic advances will remain in an incomplete information space until knowledge infrastructure gaps are closed.
SN - 1663-9812
ER -