← Back to Clinical Trials
Recruiting NCT07795645

NCT07795645 Validating a Medical AI Fine-Tuning Platform for Major Diseases

◆ AI Clinical Summary
Plain-language summary for patients
Clinical Trial Summary
NCT ID NCT07795645
Status Recruiting
Phase
Sponsor Beijing Friendship Hospital
Condition Chronic Gastritis
Study Type OBSERVATIONAL
Enrollment 96,000 participants
Start Date 2024-11-01
Primary Completion 2027-08-31

Eligibility & Interventions

Sex All sexes
Min Age 18 Years
Max Age N/A
Study Type OBSERVATIONAL
Interventions
Smart Medical Big Data Dataset ConstructionLarge-Scale Medical Knowledge Graph ConstructionMulti-Disease Medical Large Model Fine-Tuning Platform Construction and Fine-Tuning

Eligibility Fast-Check

Enter your details for a quick preliminary check. This does not replace medical advice.

What to Expect as a Participant

This is an observational study. You will not receive an experimental treatment; researchers will collect data based on your existing condition or standard treatment.

This trial targets 96,000 participants in total. It began in 2024-11-01 with a primary completion date of 2027-08-31.

⚠ This information is for research awareness only. Always consult your physician before joining any clinical trial. Participation is voluntary and you may withdraw at any time.

Brief Summary

The goal of this observational study is to leverage the abundant patient resources and standardized medical records from Beijing Friendship Hospital, Xuanwu Hospital, and Beijing Anzhen Hospital, combined with the existing data and knowledge platform of guidelines, consensus, medical literature, and dialogue data from Beijing Haitian Ruisheng Science Technology Co.,Ltd, with Beijing Zhilan Medical Technology Co., Ltd. conducting the fine-tuning, optimization, and validation of the medical large language model. The model is fine-tuned according to the consultation and diagnostic needs of different departments to improve the quality and efficiency of hospital medical services, enhance intelligence, and elevate the level of medical care. Through deployment to hospitals at all levels, it aims to achieve standardized services and support graded diagnosis and treatment. The overall research includes medical big data construction, medical knowledge graph construction, medical large model training and fine-tuning, and large model application platform development and deployment.

Eligibility Criteria

Inclusion Criteria: * Retrospective Historical Medical Records: patients with stomach, cardiovascular and cerebrovascular diseases from September 2014 to August 2024 were enrolled. (1) Age ≥ 18 years; (2) Diagnosed with any of the following: chronic gastritis, gastric cancer, gastro esophageal reflux, coronary artery disease, or stroke. * Prospective Historical Medical Records: Patients with stomach, cardiovascular and cerebrovascular diseases from September 2024 and August 2027 are enrolled.Outpatient and emergency records are used for large model training, and inpatient records are used for both training and internal validation. Inpatient records are allocated to the training set and internal validation set at a 3:1 ratio. Block randomization is used to reduce bias. Each sample is assigned a raw random number uniformly distributed between 0 and 1. Under the block design, each block contains 4 samples. Within each block, samples are ranked by the raw random number and assigned a random code from 1 to 4. The randomization schedule is prepared by a statistician on a computer system before the start of the study and printed on opaque, sealed envelopes. 1. Age ≥ 18 years; (2) Clinically diagnosed with one of the following: chronic gastritis, gastric cancer, gastro esophageal reflux, coronary artery disease, or stroke; (3) Patient or legally authorized representative able to understand the study and provide informed consent; (4) Clinically stable and able to complete the study procedures Exclusion Criteria: * Retrospective Historical Medical Records: (1) Records with information that cannot be correctly read due to modification or smudging; (2) Examination reports that are smudged or damaged, making them uninterpretable by the large model. * Prospective Historical Medical Records: (1) Severe psychiatric disorders (e.g., depression, mania, epilepsy, schizophrenia); (2) Judged by the investigator to be unable to comply with study procedures; (3) Poor audio quality due to accent or recording issues that prevents accurate data capture; (4) Laboratory or imaging reports that are smudged or damaged, making them uninterpretable by the model * Withdrawal Criteria: Prospective Historical Medical Records: 1. Participant requests to withdraw from the study during the research process 2. Investigator determines that the study should be terminated based on consideration of the participant's best interests

Contact & Investigator

Central Contact

Zhi zheng, Attending surgon

✉ zhengzhi@ccmu.edu.cn

📞 +86-13811132175

Frequently Asked Questions

Who can join the NCT07795645 clinical trial?

This trial is open to participants of all sexes, aged 18 Years or older, studying Chronic Gastritis. Full inclusion and exclusion criteria are listed in the Eligibility Criteria section. Always confirm your eligibility with the research team before applying.

Is NCT07795645 currently recruiting?

Yes, NCT07795645 is actively recruiting participants. Contact the research team at zhengzhi@ccmu.edu.cn for enrollment information.

Where is the NCT07795645 trial being conducted?

This trial is being conducted at Beijing, China.

Who is sponsoring the NCT07795645 clinical trial?

NCT07795645 is sponsored by Beijing Friendship Hospital. The trial plans to enroll 96,000 participants.

Related Trials

ClinicalMetric — Independent clinical trial intelligence platform. Not affiliated with NIH, ClinicalTrials.gov, the U.S. FDA, or any pharmaceutical company, hospital, or clinical research organization. Trial data is sourced from ClinicalTrials.gov for informational purposes only and does not constitute medical advice. Do not make any treatment, enrollment, or health decisions based solely on information found here — always consult a qualified healthcare professional. Full Disclaimer  ·  Last Reviewed: September 2026  ·  Data Methodology