ArticleJMIR medical informatics2025
Leveraging GPT-4o for Automated Extraction and Categorization of CAD-RADS Features From Free-Text Coronary CT Angiography Reports: Diagnostic Study.
Article in JMIR medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Benchmarking 54 large language model configurations for CAD-RADS scoring: open-weight models approach human-level agreement.The international journal of cardiovascular imaging · 2026Article
- Retrieval-augmented generation-enhanced large language models for comprehensive CAD-RADS 2.0 categorization from structured coronary CTA reports.Radiologie (Heidelberg, Germany) · 2026Article
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8 authors.
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Abstract
Background: Despite the Coronary Artery Reporting and Data System (CAD-RADS) providing a standardized approach, radiologists continue to favor free-text reports. This preference creates significant challenges for data extraction and analysis in longitudinal studies, potentially limiting large-scale research and quality assessment initiatives. Objective: To evaluate the ability of the generative pre-trained transformer (GPT)-4o model to convert real-world coronary computed tomography angiography (CCTA) free-text reports into structured data and automatically identify CAD-RADS categories and P categories. Methods: This retrospective study analyzed CCTA reports from January 2024 and July 2024. A subset of 25 reports was used for prompt engineering to instruct the large language models (LLMs) in extracting CAD-RADS categories, P categories, and the presence of myocardial bridges and noncalcified plaques. Reports were processed using the GPT-4o API (application programming interface) and custom Python scripts. The ground truth was established by radiologists based on the CAD-RADS 2.0 guidelines. Model performance was assessed using accuracy, sensitivity, specificity, and F1-score. Intrarater reliability was assessed using Cohen κ coefficient. Results: Among 999 patients (median age 66 y, range 58-74; 650 males), CAD-RADS categorization showed accuracy of 0.98-1.00 (95% CI 0.9730-1.0000), sensitivity of 0.95-1.00 (95% CI 0.9191-1.0000), specificity of 0.98-1.00 (95% CI 0.9669-1.0000), and F1-score of 0.96-1.00 (95% CI 0.9253-1.0000). P categories demonstrated accuracy of 0.97-1.00 (95% CI 0.9569-0.9990), sensitivity from 0.90 to 1.00 (95% CI 0.8085-1.0000), specificity from 0.97 to 1.00 (95% CI 0.9533-1.0000), and F1-score from 0.91 to 0.99 (95% CI 0.8377-0.9967). Myocardial bridge detection achieved an accuracy of 0.98 (95% CI 0.9680-0.9870), and noncalcified coronary plaques detection showed an accuracy of 0.98 (95% CI 0.9680-0.9870). Cohen κ values for all classifications exceeded 0.98. Conclusions: The GPT-4o model efficiently and accurately converts CCTA free-text reports into structured data, excelling in CAD-RADS classification, plaque burden assessment, and detection of myocardial bridges and calcified plaques.
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