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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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Retrieval-augmented generation-enhanced large language models for comprehensive CAD-RADS 2.0 categorization from

Esat Kaba1, Yusuf Çubukçu2, Burak Uzunibrahimoğlu2

  • 1Department of Radiology, Training and Research Hospital, Recep Tayyip Erdogan University, 53100, Rize, Türkiye. esatkaba04@gmail.com.

Radiologie (Heidelberg, Germany)
|February 20, 2026
PubMed
Summary

Retrieval-augmented generation (RAG)-enhanced large language models (LLMs) significantly improve accuracy in extracting Coronary Artery Disease Reporting and Data System (CAD-RADS) components from coronary computed tomography angiography (CCTA) reports. These RAG-based LLMs show potential for automated and standardized CCTA reporting in clinical radiology.

Keywords:
Coronary StenosisCoronary computed tomography angiographyLarge language modelsManagementPlaque burden

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Radiology Informatics
  • Natural Language Processing in Healthcare

Background:

  • Structured reporting in coronary computed tomography angiography (CCTA) is crucial for accurate diagnosis and management.
  • Large Language Models (LLMs) offer potential for automating data extraction from clinical reports.
  • Evaluating LLM performance in extracting specific components and recommendations from CCTA reports is necessary.

Purpose of the Study:

  • To assess the performance of standard and retrieval-augmented generation (RAG)-based LLMs in extracting components and management recommendations from CCTA reports.
  • To compare the accuracy of different LLMs, including ChatGPT-5, NotebookLM, and a RAG-adapted ChatGPT-5, against expert radiologists' assessments.
  • To evaluate the utility of LLMs in adhering to the Coronary Artery Disease Reporting and Data System (CAD-RADS 2.0) guidelines.

Main Methods:

  • Analysis of 320 structured CCTA reports using three LLMs: standard ChatGPT-5, NotebookLM (RAG-based), and ChatGPT-5-RAG.
  • Extraction of CAD-RADS category, plaque burden, high-risk plaque (HRP), modifiers, full score, and management recommendations.
  • Comparison of LLM outputs against a reference standard established by two expert cardiovascular radiologists.

Main Results:

  • ChatGPT-5-RAG demonstrated superior accuracy across all evaluated CAD-RADS 2.0 components, including classification, plaque burden, HRP detection, and modifiers.
  • Standard ChatGPT-5 exhibited the weakest performance among the tested models.
  • While agreement on management recommendations was low, ChatGPT-5-RAG and NotebookLM achieved near-perfect qualitative ratings.

Conclusions:

  • RAG-enhanced LLMs significantly improve the accuracy and reliability of extracting CAD-RADS 2.0 components and generating management recommendations.
  • RAG-based LLMs represent promising tools for automating and standardizing CCTA reporting within clinical radiology workflows.
  • The explainability and innovation offered by RAG-based LLMs can enhance clinical decision-making in cardiovascular imaging.