Diagnostic Performance of Expert Physicians Versus General-Purpose Artificial Intelligence Using Standardized Static Coronary CT Images: A Dual-Reference Validation

Background Coronary CT angiography (CCTA) is a first-line diagnostic modality for coronary artery disease (CAD), yet its interpretation requires significant expert experience. Although general-purpose multimodal artificial intelligence (GP-AI) models have shown promise in text-based medical tasks, their visual diagnostic performance in evaluating complex CCTA data remains poorly defined. Methods This single-center retrospective study included 63 patients (252 vessel-based image sets) who underwent both CCTA and invasive coronary angiography. Expert physician consensus and four frontier GP-AI models (GPT-4o, Gemini 2.5, Claude 3.5 Sonnet, and Grok 4) evaluated identical standardized static images using a zero-shot approach with default generation parameters. Obstructive disease was defined as ≥ 50% luminal stenosis. Diagnostic performance was validated against expert consensus for plaque characterization and quantitative coronary angiography (QCA) for stenosis severity. Results Expert consensus demonstrated robust agreement with QCA across all coronary territories (kappa = 0.774–0.933, p < 0.001). In contrast, a marked performance disparity was observed for the GP-AI models; none achieved statistically significant agreement with QCA in the prognostically critical left anterior descending (LAD) or left main coronary arteries (LMCA) (p > 0.05). While Gemini 2.5 showed a moderate correlation in the right coronary artery (ICC = 0.515), overall continuous stenosis assessment and plaque characterization remained uniformly limited and clinically unreliable across all models. Conclusion Expert physician interpretation remains the reference standard for CCTA. Current frontier GP-AI models are not suitable for independent clinical interpretation of coronary imaging, particularly in anatomically complex segments. These findings emphasize that general visual reasoning cannot yet replace domain-specific cardiovascular AI solutions or expert clinical judgment in specialized radiological tasks.