What will be the next step of LLMs in TCM? A narrative review
10.1097/st9.0000000000000109
- Author:
Siyi CHEN
1
;
Ruikang ZHONG
1
;
Wenzheng ZHANG
1
;
Zexing LI
1
;
Yisha SU
1
;
Lei GAO
2
;
Kaiwen HU
2
Author Information
1. Graduate School, Beijing University of Chinese Medicine, Beijing, China
2. Oncology Department, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China
- Publication Type:Journal Article
- Keywords:
Large language models;
Narrative review;
Traditional Chinese medicine
- From:
Science of Traditional Chinese Medicine
2026;4(2):111-118
- CountryChina
- Language:English
-
Abstract:
Large language models (LLMs) offer a modern approach to help inherit traditional Chinese medicine (TCM). This article discussed the progress of LLM applications in TCM and proposed future development directions by reviewing the existing research. We have found that LLMs and related technologies have excellent applications and performance in the management of TCM knowledge and data. They are often applied in information extraction, knowledge graph construction, and data standardization processing. However, data quality and security issues need to be given more attention. In clinical diagnosis and treatment, LLMs can imitate the thinking of TCM by disassembling and reconstructing its diagnostic process and can achieve functions such as prescription recommendation and question and answer (Q&A). However, this approach involves LLMs making inferences and predictions based on existing corpora and thus may not flexibly handle complex environments and tasks. Moreover, the current evaluation criteria for TCM LLMs can be summarized into 3 categories: general evaluation metrics, technical framework evaluation, and evaluation criteria for the characteristics of TCM (such as consistency rates of prescriptions and diagnostic suggestions). However, the lack of a unified and standardized evaluation system hinders the clinical application of TCM LLMs. The future progress of TCM LLMs should focus on the 3 aforementioned critical aspects to achieve technological breakthroughs. In addition, we are promoting the research on vertical TCM LLMs and application terminals. We believe this will bring new ideas to the research on TCM LLMs.
