Background: While intravascular imaging, particularly optical coherence tomography (OCT), improves percutaneous coronary intervention (PCI) outcomes, its interpretation is operator-dependent. General-purpose artificial intelligence (AI) shows promise but lacks domain-specific reliability. We evaluated the performance of CA-GPT, a novel large model deployed on an AI-OCT system, against that of the general-purpose ChatGPT-5 and junior physicians for OCT-guided PCI planning and assessment. Methods: In this single-center analysis of 96 patients who underwent OCT-guided PCI, the procedural decisions generated by the CA-GPT, ChatGPT-5, and junior physicians were compared with an expert-derived procedural record. Agreement was assessed using ten pre-specified metrics across pre-PCI and post-PCI phases. Results: For pre-PCI planning, CA-GPT demonstrated significantly higher median agreement scores (5[IQR 3.75-5]) compared to both ChatGPT-5 (3[2-4], P<0.001) and junior physicians (4[3-4], P<0.001). CA-GPT significantly outperformed ChatGPT-5 across all individual pre-PCI metrics and showed superior performance to junior physicians in stent diameter (90.3% vs. 72.2%, P<0.05) and length selection (80.6% vs. 52.8%, P<0.01). In post-PCI assessment, CA-GPT maintained excellent overall agreement (5[4.75-5]), significantly higher than both ChatGPT-5 (4[4-5], P<0.001) and junior physicians (5[4-5], P<0.05). Subgroup analysis confirmed CA-GPT's robust performance advantage in complex scenarios. Conclusion: The CA-GPT-based AI-OCT system achieved superior decision-making agreement versus a general-purpose large language model and junior physicians across both PCI planning and assessment phases. This approach provides a standardized and reliable method for intravascular imaging interpretation, demonstrating significant potential to augment operator expertise and optimize OCT-guided PCI.

翻译：背景：虽然血管内成像技术（尤其是光学相干断层扫描（OCT））可改善经皮冠状动脉介入治疗（PCI）的临床结果，但其解读结果依赖于术者经验。通用人工智能（AI）虽展现出潜力，但缺乏领域特异性可靠性。本研究评估了部署于AI-OCT系统的新型大模型CA-GPT，与通用模型ChatGPT-5及初级医师在OCT引导的PCI规划与评估任务中的表现。方法：本研究为单中心分析，纳入96例接受OCT引导PCI的患者，将CA-GPT、ChatGPT-5及初级医师生成的术中决策与专家制定的手术记录进行对比。通过十项预设指标，在PCI术前与术后阶段评估决策一致性。结果：在PCI术前规划阶段，CA-GPT的中位一致性评分（5[IQR 3.75-5]）显著高于ChatGPT-5（3[2-4], P<0.001）和初级医师（4[3-4], P<0.001）。CA-GPT在所有术前单项指标上均显著优于ChatGPT-5，并在支架直径（90.3% vs. 72.2%, P<0.05）和长度选择（80.6% vs. 52.8%, P<0.01）方面表现优于初级医师。在PCI术后评估阶段，CA-GPT保持优异的总体一致性（5[4.75-5]），显著高于ChatGPT-5（4[4-5], P<0.001）和初级医师（5[4-5], P<0.05）。亚组分析证实CA-GPT在复杂病变场景中具有稳健的性能优势。结论：基于CA-GPT的AI-OCT系统在PCI规划与评估阶段，相较于通用大语言模型及初级医师，均展现出更优的决策一致性。该方法为血管内成像解读提供了标准化、可靠的解决方案，显著展现出增强术者专业能力、优化OCT引导PCI临床实践的潜力。