This research aims to evaluate a comprehensive AI-driven workflow for both clinical data extraction and diagnostic classification in coronary artery disease (CAD). Leveraging OCR and Large Language Models (LLMs), the system is designed to extract ten key clinical parameters (such as LVEF and lab results) and provide diagnostic subtypes (UA, STEMI, NSTEMI, CCS) directly from unstructured inpatient records. A man-machine comparative trial will be conducted using a test set of 308 patients, where the performance of the LLM-based workflow will be benchmarked against the average diagnostic accuracy and processing time of seven clinical physicians. The findings will provide evidence for the feasibility of using LLMs to enhance clinical data structuring and diagnostic efficiency in cardiology.

Inclusion Criteria:

1. Patients aged 18 years and older.
2. Clinical records of patients who were previously enrolled in the AIM-CHD (for the pilot/prompt optimization set) or SMART-CHD (for the internal validation cohort) studies.
3. Patients diagnosed with, or suspected of having, coronary artery disease (CAD), including subtypes: Unstable Angina (UA), STEMI, NSTEMI, and Chronic Coronary Syndrome (CCS).

Exclusion Criteria:

1. Clinical records with severe data fragmentation or missing more than 50% of the key clinical indicators.
2. Handwritten medical records or low-quality scans that are illegible for Optical Character Recognition (OCR) processing.
3. Duplicate records or records with conflicting "Gold Standard" labels that cannot be reconciled by the expert committee.

he study population consists of 308 patients diagnosed with various subtypes of coronary artery disease (CAD). The cohort is derived from two major clinical studies: the AIM-CHD study (for pilot testing and prompt optimization) and the SMART-CHD study (for internal validation), both conducted at Fuwai Hospital. Additionally, an external validation cohort is included, comprising patients from 8 independent clinical sub-centers across China to ensure geographical and institutional diversity. The population covers a spectrum of CAD presentations, including Unstable Angina (UA), STEMI, NSTEMI, and Chronic Coronary Syndrome (CCS), providing a robust dataset for evaluating AI-driven diagnostic and data extraction performance.

To protect patient privacy and comply with the data management policies of the participating institutions (Fuwai Hospital and sub-centers), individual participant data will not be made publicly available. However, aggregated study results and statistical analyses will be included in the final publication.