ArticleInsights into imaging2026
Comparative evaluation of large language models for generating CAD-RADS 2.0-compliant diagnostic conclusions in cardiac CT reports.
Article in Insights into imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness, Safety, and Workflow Burden of Large Language Model-Based Medical Report Generation: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Benchmarking 54 large language model configurations for CAD-RADS scoring: open-weight models approach human-level agreement.The international journal of cardiovascular imaging · 2026Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesCoronary computed tomography angiography (CCTA) has become a cornerstone in non-invasive CAD diagnosis and risk stratification. To standardize reporting and improve clinical decision-making, the CAD-RADS 2.0 system was introduced. This study evaluates the performance of four LLMs, GPT-4o, Gemini 2.0 Flash, DeepSeek V, and Copilot in generating CAD-RADS 2.0-compliant conclusions from standardized CCTA reports. MATERIALS AND
methodsA total of 196 anonymized CCTA reports were retrospectively analyzed. Each LLM was prompted to provide CAD-RADS 2.0 classifications and follow-up recommendations. Ground truth labels were assigned by a senior radiologist. Performance metrics (accuracy, precision, recall, F1-score), execution times, and agreement (Cohen's kappa) with expert interpretation were computed. Interobserver agreement between junior and senior radiologists was also assessed.
resultsLLMs demonstrated good-to-excellent agreement with expert classifications: DeepSeek V (κ = 0.771), Copilot (κ = 0.761), GPT-4o (κ = 0.759), and Gemini 2.0 Flash (κ = 0.634). DeepSeek V achieved the highest accuracy (91.83%). Intra-model consistency was perfect (κ = 1). However, LLMs failed to assign CAD-RADS modifiers. ChatGPT-4o provided the most accurate follow-up recommendations (71.94%). All LLMs outperformed radiologists in execution time (3-9 s vs. 15-20 s; p < 0.05).
conclusionsGeneric LLMs demonstrate promising performance in automating CAD-RADS 2.0 classification from CCTA reports. However, limitations in modifier assignment and recommendation accuracy highlight areas for refinement before clinical integration. CRITICAL RELEVANCE STATEMENT: This study explores the potential of large language models to facilitate standardized CAD-RADS 2.0 reporting from coronary CT angiography, highlighting a possible avenue to support workflow efficiency and clinical decision-making in non-invasive coronary artery disease evaluation. KEY POINTS: LLMs demonstrated strong potential in automating CAD-RADS 2.0-compliant structured reporting for CCTA. LLMs could significantly enhance efficiency in radiological reporting. LLMs need further optimization before clinical integration.
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