Building a Traditional Chinese Medicine Clinical Diagnosis and Treatment Database

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

80000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-08-01

Study Completion Date

2026-08-15

Brief Summary

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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.

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Detailed Description

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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.

Conditions

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Medicine, Chinese Traditional Artificial Intelligence

Study Design

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Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Study Groups

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Traditional Chinese Medicine Tongue Image Group

Internally, using random allocation, divided into training group and validation group

Observational study, non intervention

Intervention Type OTHER

Observational study, non intervention

Traditional Chinese Medicine Constitution Data Group

Internally, using random allocation, divided into training group and validation group

Observational study, non intervention

Intervention Type OTHER

Observational study, non intervention

Traditional Chinese Medicine Doctor Patient Dialogue Data Group

Data used for fine-tuning traditional Chinese medicine models

Observational study, non intervention

Intervention Type OTHER

Observational study, non intervention

Interventions

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Observational study, non intervention

Intervention Type OTHER

Eligibility Criteria

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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.

Minimum Eligible Age

18 Years

Maximum Eligible Age

85 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes