Validating a Medical AI Fine-Tuning Platform for Major Diseases

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

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

Conditions7

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