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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Traditional Chinese Medicine Tongue Image Group | Internally, using random allocation, divided into training group and validation group |
| |
| Traditional Chinese Medicine Constitution Data Group | Internally, using random allocation, divided into training group and validation group |
| |
| Traditional Chinese Medicine Doctor Patient Dialogue Data Group | Data used for fine-tuning traditional Chinese medicine models |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Observational study, non intervention | Other | Observational study, non intervention |
|
| Measure | Description | Time Frame |
|---|---|---|
| Development of a tongue image-based machine learning tool |
| 20 months |
| Measure | Description | Time Frame |
|---|---|---|
| TCM Constitution Multimodal Model |
|
| Measure | Description | Time Frame |
|---|---|---|
| Traditional Chinese Medicine (TCM) Doctor-Patient Dialogue Database |
| 20 months |
Inclusion Criteria:
Exclusion Criteria:
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There are no specific restrictions on the disease, gender, or health status of the enrolled population.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Yulong Zhang, Doctor | Contact | 18810550602 | zhongxiyi1101@163.com |
| Name | Affiliation | Role |
|---|---|---|
| Qi Zeng, Doctor | Fifth Affiliated Hospital, Sun Yat-Sen University | Principal Investigator |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 38627559 | Background | 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. | |
| 36825238 |
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| ID | Term |
|---|---|
| D019370 | Observation |
| ID | Term |
|---|---|
| D008722 | Methods |
| D008919 | Investigative Techniques |
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| 20 months |
| Background |
| 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. Development of a tongue image-based machine learning tool for the diagnosis of gastric cancer: a prospective multicentre clinical cohort study. EClinicalMedicine. 2023 Feb 6;57:101834. doi: 10.1016/j.eclinm.2023.101834. eCollection 2023 Mar. |
| 38503097 | Background | 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. |