TCM LLM · Surveys
TCM LLM surveys
Surveys of Traditional Chinese Medicine large language models: knowledge organization, assisted diagnosis, multimodal four examinations, agents, and evaluation.
从大语言模型到智能体（兰州大学学报医学版综述）
Chinese-language systematic review organized around the LLM-to-agent transition for TCM clinical assisted diagnosis and treatment (J. Lanzhou Univ. Med. Sci. 2026;52(4):49-57).
Agentic and Knowledge-Grounded LLMs in TCM（预注册）
A review plan registered on OSF in July 2026. It will look at agentic and knowledge-grounded language models in TCM through evidence maps, text mining, and whether they are ready for clinical use. The review is still underway.
中医大模型关键技术综述（IJPRAI）
Survey of key technologies for TCM LLMs: knowledge organization, aided diagnosis, and clinical decision support (formally published in IJPRAI, World Scientific).
AI驱动中医诊断智能化综述（JTCMS）
Survey on multimodal fusion and LLMs for intelligent four-diagnosis in TCM: applications, challenges and outlook (JTCMS).
人工智能驱动下的中医智能诊疗研究进展与挑战
Chinese review structured on the six-step TCM diagnosis-treatment chain (four diagnoses, pattern differentiation, prescription, outcome prediction), contrasting supervised/unsupervised/RL/deep-learning paradigms (Shanghai J. TCM 2026;60(1)).
评估药物–草药相互作用的人工智能工具
Review of artificial-intelligence models and tools for assessing drug–herb interactions.
肿瘤治疗中的草药–化学药相互作用
Review of herb–drug interactions in oncology, covering pharmacodynamic and pharmacokinetic mechanisms and risk prediction.
计算机辅助穴位定位系统综述
Systematic review of traditional algorithms and deep learning for acupoint localization across anatomical regions.
深度学习穴位定位综述
Review of deep-learning acupoint localization, grouped by body region, architecture, strategy, and image modality, including datasets and metrics.
东亚传统医学如何进入国际卫生信息标准
Scoping review of English reports from January 2016 to March 2026 on how Korean medicine, TCM, and Kampo concepts are mapped into mainstream health-informatics standards.
中医为何需要可解释人工智能
Argues that TCM AI is limited by a mismatch between statistical models and syndrome-based reasoning, so explanations are required.
面向临床的中西药联用安全性数智评价
A clinical framework for safety of combined Chinese and western drugs: prediction, risk detection, mechanism checks, and decision support before and during use.
中药计算毒性研究小综述
Mini-review of in silico toxicity studies of traditional Chinese herbal medicine.
中药安全性研究：方法、应用与展望
Review of why TCM toxicity is hard to study: multi-component formulas and unpredictable drug–body interactions, unlike chemical or biological drugs.
Artificial Intelligence in Traditional Chinese Medicine: Multimodal Fusion and Machine Learning for Enhanced Diagnosis and Treatment Efficacy
Reviews multimodal fusion and machine learning for AI-assisted diagnosis and treatment in traditional Chinese medicine.
AI-driven multi-scale target analysis of traditional Chinese medicine: From the pharmacological effects of single compounds to the synergistic mechanisms of formulae
This review of multi-scale traditional Chinese medicine target analysis lists the development of TCM-specific large language models among its future directions, in order to decode TCM pharmacological logic.
XiangShu Omics: A Framework Based on the Spatio–Temporal Field Rhythms of Human Life and Systems Intelligence Methodology
This review says it is the first to systematically construct XiangShu omics, and states that its large model integrates large language models grounded in the classical logic of principle, method, formula, and medicine, such as Shu-Zhi Qihuang.
From Empirical Wisdom to Computational Intelligence: A Knowledge-Data Dual-Driven Paradigm for Modernizing Traditional Chinese Medicine
The paper proposes a knowledge–data dual-driven paradigm that turns implicit traditional Chinese medicine experience into computable structure with large-scale knowledge graphs and TCM-specific large language models, and digitizes the four examinations into a multimodal TCM phenomics.
Domestic and International Research Trends in Artificial Intelligence Applications in Korean Medicine: An Analysis from 2019 to 2025
The authors systematically reviewed 23 Korean-medicine artificial-intelligence studies from 2019 to 2025, ten domestic and thirteen international, and report that recent work increasingly uses large language models and other generative AI.
Artificial intelligence: A catalyst for the modernization of traditional Chinese medicine
This scoping review searched PubMed, Web of Science, and CNKI through 2025 and states that deep learning and large language models show promising accuracy on tongue-image analysis and syndrome differentiation, in some studies approaching expert-level performance.
Artificial Intelligence in Traditional Chinese Medicine: Bridging Ancient Practice and Future Innovation
This review analyzes traditional Chinese medicine large language models along construction, application, challenges, and prospects, noting that systems such as Qibo and MedChatZH already exist while earlier reviews lacked a synthesis of shared characteristics.
2026: 中医智能诊断的跨学科融合与发展趋势
Discusses interdisciplinary integration and development trends of intelligent diagnosis in TCM.
2026: 面向中医药证据知情决策的数字智能循证
Discusses digital intelligent evidence-based methods for evidence-informed decisions in Chinese medicine.
2026: 数字化与智能化时代的中医药机遇与挑战
Discusses opportunities and challenges for TCM as AI moves from expert systems to deep learning and large language models.
Dong et al. 2026: 中医处方推荐计算方法综述
Reviews computational herbal-prescription recommendation and how it can support TCM clinical decisions.
Wang et al. 2026: 重思中医大模型评测
Argues that TCM LLM evaluation still lacks a quality floor: unfiltered classics and web text can contain outdated advice, and a single case is not a benchmark.
Sun et al. 2026: 生成模型在中医药中的进展与应用
Reviews advances and applications of generative models in traditional Chinese medicine.
Liu et al. 2025: 网络药理学推动大模型用于中医药研究
Reviews how network pharmacology and large language models jointly explain multi-target mechanisms of TCM formulas.
Yin et al. 2025: 数据挖掘、大模型与多模态融合的中医AI综述
Reviews AI for TCM across data mining, large language models, and multimodal fusion, aimed at multi-component herbal formulas.
Cong H et al. 中医古籍知识挖掘范围综述（OSF 预注册）
A scoping-review plan registered on OSF on 15 April 2026. It will map how knowledge graphs, large language models, and the two together are used to draw knowledge out of TCM classics, following PRISMA-ScR. The review is still underway.
Cai R et al. 中医辨证分类范围综述（OSF 预注册）
A scoping-review plan registered on OSF on 3 April 2026. It will map how large language models do TCM syndrome differentiation, syndrome elements, and syndrome reasoning, and will be written to PRISMA-ScR. The review is still underway.
AI in TCM: multimodal data to pharmacology and clinical decision（PRMCM 综述）
An English review that runs from multimodal data and pharmacology to clinical decisions. Pharmacological Research - Modern Chinese Medicine, 2026.
LLM-Based Multi-Agent Systems for Clinical Workflows（ACL 2026，邻近）
Adjacent ACL 2026 survey of workflow-level multi-agent clinical systems with a four-layer evaluation stack; no TCM coverage but methodologically isomorphic process-evaluation claims.
医学大语言模型的研发与应用系统综述（智能系统学报，邻近）
Adjacent systematic review of 129 medical-domain LLMs (to 2024-06) and four clinical application categories; methodologically comparable search protocol.
医疗领域的大型语言模型综述（智能系统学报，邻近）
Adjacent Chinese general survey of medical LLMs (training pipeline, strategies, scenarios, challenges), a superset-context reference for TCM LLM surveys.
Intelligent Question-Answering Systems in Healthcare（Healthcare，邻近）
Adjacent review (not TCM-specific): 2018-2025 healthcare QA survey with CiteSpace bibliometrics, explicitly covering TCM formula-development scenarios (Healthcare 2025).
人工智能实现中医四诊的发展现状、问题及解决路径（中华中医药学刊）
Reviews where AI stands across the four TCM examinations. Inspection photographs the face, tongue, sublingual vessels, lips, and eyes and quantifies them with segmentation and color models; auscultation and olfaction capture abnormal sounds and odors and analyze them with neural networks and electronic noses. Published in Chinese Archives of Traditional Chinese Medicine, 2025, 43(11).
人工智能赋能中医数字化诊断：现状与挑战（中华中医药学刊）
A short essay on what AI can do for digital TCM diagnosis, and on data quality, explanations, and joining the models to TCM theory. Published in Chinese Archives of Traditional Chinese Medicine.
AI for Spleen-Stomach Disorders in TCM（Curr Med Sci）
Single-disease-area (spleen-stomach) review of KG plus intelligent diagnosis/treatment with a symptom-syndrome-disease-formula framework (Curr. Med. Sci. 2025;45(6)).
AI and Big Data in TCM Standardization and Internationalization（Chin Med Cult）
An essay on how AI and big data can help standardize traditional Chinese medicine and carry it abroad. Chinese Medicine and Culture, 2026, volume 9, issue 3.
AI empowers the innovation of TCM（J Integr Med 评论）
Single-author perspective on AI for TCM innovation: classics mining, diagnosis standardization, drug R&D cycles (J. Integr. Med. 2026).
古籍知识图谱×多智能体融合综述（Chin Med）
Challenges-and-prospects review of knowledge-graph construction over ancient TCM classics, first to frame multi-agent convergence in this area (Chin. Med. 2025;20:168).
The integration of machine learning into TCM（J Pharm Anal）
Review of machine-learning integration into TCM along diagnostic objectification and mechanism-elucidation lines (J. Pharm. Anal. 2025;15(8):101157).
Deep learning in TCM（J Integr Med）
Single-technology review of deep learning in TCM: medical imaging, herbal material research, data mining (J. Integr. Med. 2026;24(4):471-480).
AI in TCM: Unraveling Herbal Medicine's Mechanisms（Research）
Broad AI-in-TCM review arguing AI should move beyond correlational analysis toward reconstructing the biological logic of syndrome differentiation and formula compatibility (Research 2026;9:1224).
多模态大模型驱动舌脉面诊智能化综述（Springer 书章）
The only review text dedicated to multimodal-LLM-driven tongue, pulse, and facial diagnosis in TCM (Springer CCIS book chapter; weaker peer review than journals).
Lukman et al. 2007: 中医计算方法综述
Foundational survey of computational methods for TCM (expert systems, ML, data mining).
Gu & Chen 2013: 生物信息学遇见中医
Historical review of bioinformatics meeting TCM (omics and text mining).
Zhao et al. 2015: 中医患者分类进展（ML 视角）
Review of ML-driven advances in patient classification for TCM.
Chu et al. 2020: 中医定量知识表示模型综述
Review of quantitative knowledge representation models of TCM (ontologies, rules, statistics).
Zhang et al. 2021: 计算中医诊断文献综述
Literature survey of computational TCM diagnosis — symptom acquisition, pattern modeling, and systems.
Tian et al. 2024: 四诊机器学习综述
Review of machine learning for TCM four diagnoses — inspection, auscultation-olfaction, inquiry, and palpation.
Qu et al. 2024: 中医知识图谱综述
Review of knowledge graphs in TCM — analysis, construction, applications, and prospects.
Song et al. 2024: AI 辅助中医辨证关键问题与技术挑战
Strategic-study review of key issues in AI-assisted TCM syndrome differentiation — multimodal fusion, symptom association, pattern quantification and reasoning, and TCM LLMs.
Su et al. 2024 — Review of AI in TCM diagnosis and treatment (Chinese)
Chinese-language review of three AI stages in TCM care — expert systems, ML, and deep learning — with challenges.
Li et al. 2024: Research progress and application prospects of large language models in TCM
Chinese-language review of TCM LLM pipelines, frontier techniques (prompting/RAG/RLHF), and application prospects.
Yip et al. 2025: 中西医结合 LLM 进展与挑战
Review of LLMs in integrative medicine — progress, challenges, and opportunities.
Wang et al. 2025: AI 驱动中医诊断模型进展
Overview of AI-driven TCM diagnostic models (four-diagnosis objectification, pattern differentiation).
Meng et al. 2025: 大模型+虚拟细胞助力中医变革
Review of large models and virtual cells aiding modern analysis of stroke treatment with TCM formulas.
Zhang et al. 2025: 中医 LLM 短综述与展望
Short survey and outlook on TCM LLM models and tasks.
Shataer et al. 2025: LLM 在中医应用（State-of-the-Art Review）
State-of-the-art review scanning TCM LLM application scenarios (care, education, translation, research).
Guo et al. 2025: GPT 能否加速中医智能诊疗（综述+实证）
Survey plus empirical analysis of whether GPTs can accelerate intelligent TCM diagnosis and treatment.
Chen et al. 2025: 中医大语言模型系统综述
Systematic review of 10 studies (to mid-2024) on LLMs in TCM generative tasks.
Ren et al. 2025: 中医大语言模型（Scoping Review）
Arksey-O'Malley scoping review (29 studies to 2024-04) covering knowledge management, assisted care, and exam accuracy.
Lu et al. 2026: 深度学习中医诊断方法学质量审计
Systematic review and validation-gap analysis of deep learning for TCM disease diagnosis.
Wu et al. 2026: AI 在中药材中的应用综述
Full-stack survey of AI in TCM herbs — compounds, targets, quality control, with an LLM section.
Guo et al. 2026: AI 与多模态数据融合推动中医现代化
Panoramic AI review (ML/DL/KG/NLP/LLM) for TCM modernization with multimodal data integration.
Chen et al. 2026: LLM 在中医的下一步（叙述性综述）
Narrative review on the next step of LLMs in TCM — multimodality, agents, and clinical translation.
Xu et al. 2026: 基于 LLM 的中医智能问答系统综述
Review of intelligent TCM question-answering systems based on LLMs (KG-QA to LLM-QA and RAG).
Yao et al. 2026: LLM 与循证中医整合（Scoping Review）
PRISMA scoping review (12 studies, 2022-11 to 2026-01) on integrating LLMs with evidence-based Chinese medicine.
Han et al. 2026: LLM 在中医中的调优与临床应用（Scoping Review）
PRISMA-ScR scoping review (27 studies to 2025-05) on tuning (LoRA/CPT/RAG) and clinical application of TCM LLMs.