Building a Traditional Chinese Medicine Clinical Diagnosis and Treatment Database
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Observational study, non intervention.
- Who it may be relevant to
- Registry conditions: Medicine, Chinese Traditional, Artificial Intelligence. Basic parameters: 18 years — 85 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
Building a Traditional Chinese Medicine Clinical Diagnosis and Treatment Database: A Prospective Multicenter Cross-Sectional Study
Overview
Collecting Traditional Chinese Medicine (TCM) clinical diagnosis and treatment data, including doctor-patient dialogues, tongue diagnosis, facial diagnosis, and TCM constitution information, to construct databases for tongue diagnosis, TCM constitution, and doctor-patient dialogues. Based on artificial intelligence technology, engage in research related to the standardization and intelligentization of TCM.
Detailed description
The technological principles of large language models align with the empirical medical principles of Traditional Chinese Medicine (TCM), and the rise of large model technology can greatly promote the progress of TCM. However, there is currently a lack of clinical diagnosis and treatment databases with TCM characteristics for training TCM artificial intelligence(AI) large models.
At present, a large-scale tongue image database has not yet been established for modeling common TCM tongue appearances, thereby ensuring the accuracy and consistency of TCM diagnosis and promoting the objective standardization of TCM diagnostic development.
Considering the feedback from the subjects in clinical work that the TCM constitution survey questionnaire has a large volume, takes a long time, and has certain subjective issues, we plan to carry out a large-scale clinical observational study to optimize the process of TCM constitution identification.
Traditional Chinese Medicine (TCM) doctor-patient dialogues and medical record writing are essential entities generated during the TCM diagnosis and treatment process. Assisting in consultation, medical record generation, and treatment plan recommendations based on doctor-patient dialogues have significant clinical and research value. Therefore, we plan to collect a large number of doctor-patient dialogues and outpatient medical records to construct a doctor-patient dialogue database, preparing in advance for optimizing interactive large-scale TCM models.
In summary, the research on constructing a TCM clinical diagnosis and treatment database has important clinical and scientific research value. This will help to improve the standardization and normalization of TCM diagnosis and treatment, and also support the modernization and internationalization of TCM. By applying big data analysis and artificial intelligence technology, it is possible to delve deeper into TCM diagnosis and treatment information, providing richer and more accurate data resources for clinical decision-making and scientific research exploration in TCM.
Interventions
- Other Observational study, non intervention
Observational study, non intervention
Primary outcome measures
- Development of a tongue image-based machine learning tool [Time frame: 20 months]
Secondary outcome measures (1)
- TCM Constitution Multimodal Model [Time frame: 20 months]
Eligibility criteria
Inclusion criteria
- People who come to the hospital for physical examination and medical treatment；
- Participants voluntarily participate in the study.
Exclusion criteria
- Subjects with difficulty in tongue extension, communication, etc. who cannot cooperate with data collection；
- The researchers determined that there were other factors that may have forced the subjects to terminate the study.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Other
Study locations
Center list to be confirmed — check the primary protocol.
Publications
- Tian F, Liu D, Wei N, Fu Q, Sun L, Liu W, Sui X, Tian K, Nemeth G, Feng J, Xu J, Xiao L, Han J, Fu J, Shi Y, Yang Y, Liu J, Hu C, Feng B, Sun Y, Wang Y, Yu G, Kong D, Wang M, Li W, Chen K, Li X. Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning. Nat Med. 2024 May;30(5):1309-1319. doi: 10.1038/s41591-024-02915-w. Epub 2024 Apr 16. PMID 38627559
- Yuan L, Yang L, Zhang S, Xu Z, Qin J, Shi Y, Yu P, Wang Y, Bao Z, Xia Y, Sun J, He W, Chen T, Chen X, Hu C, Zhang Y, Dong C, Zhao P, Wang Y, Jiang N, Lv B, Xue Y, Jiao B, Gao H, Chai K, Li J, Wang H, Wang X, Guan X, Liu X, Zhao G, Zheng Z, Yan J, Yu H, Chen L, Ye Z, You H, Bao Y, Cheng X, Zhao P, Wang L, Zeng W, Tian Y, Chen M, You Y, Yuan G, Ruan H, Gao X, Xu J, Xu H, Du L, Zhang S, Fu H, Cheng X PMID 36825238
- Tan Y, Zhang Z, Li M, Pan F, Duan H, Huang Z, Deng H, Yu Z, Yang C, Shen G, Qi P, Yue C, Liu Y, Hong L, Yu H, Fan G, Tang Y. MedChatZH: A tuning LLM for traditional Chinese medicine consultations. Comput Biol Med. 2024 Apr;172:108290. doi: 10.1016/j.compbiomed.2024.108290. Epub 2024 Mar 13. PMID 38503097
Identifiers
NCT: NCT06525025 · ZDWY.ZYZLK.009