AI doctors in training: can a computer learn to diagnose stomach and heart diseases?

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

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