arXiv 2512.107022025-12-11cs.AI

COMPARE: 通过RAG增强的AI-OCT实现临床优化：与ChatGPT-5和初级操作者相比，为经皮冠状动脉介入术提供更优的决策支持

COMPARE: Clinical Optimization with Modular Planning and Assessment via RAG-Enhanced AI-OCT: Superior Decision Support for Percutaneous Coronary Intervention Compared to ChatGPT-5 and Junior Operators

Tangdu Hospital, The Fourth Military Medical University（空军军医大学唐都医院）；Research Institute of Tsinghua University in Shenzhen（深圳清华大学研究院）；The Second Affiliated Hospital, Air Force Medical University（空军军医大学第二附属医院）

Wei Fang, Chiyao Wang, Wenshuai Ma, Hui Liu, Jianqiang Hu, Xiaona Niu, Yi Chu, Mingming Zhang, Jingxiao Yang, Dongwei Zhang, Zelin Li, Pengyun Liu, Jiawei Zheng… 展开作者

Wei Fang, Chiyao Wang, Wenshuai Ma, Hui Liu, Jianqiang Hu, Xiaona Niu, Yi Chu, Mingming Zhang, Jingxiao Yang, Dongwei Zhang, Zelin Li, Pengyun Liu, Jiawei Zheng, Pengke Zhang, Chaoshi Qin, Wangang Guo, Bin Wang, Yugang Xue, Wei Zhang, Zikuan Wang, Rui Zhu, Yihui Cao, Quanmao Lu, Rui Meng, Yan Li

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AI总结：

CA-GPT通过RAG增强的AI-OCT系统在PCI规划和评估中优于ChatGPT-5和初级医生，提供更可靠的决策支持。

摘要：

背景：尽管血管内成像，特别是光学相干断层扫描（OCT），提高了经皮冠状动脉介入术（PCI）的疗效，但其解释依赖于操作者。通用人工智能（AI）显示出潜力，但缺乏领域特定的可靠性。我们评估了CA-GPT，一种新型大模型部署在AI-OCT系统上的性能，与通用的ChatGPT-5和初级医生在OCT引导的PCI规划和评估中的表现。方法：在单中心分析96名接受OCT引导PCI的患者中，比较CA-GPT、ChatGPT-5和初级医生生成的程序决策与专家生成的程序记录。使用十种预设的指标在PCI前和PCI后阶段评估一致性。结果：在PCI前规划中，CA-GPT的中位一致性得分显著高于ChatGPT-5（3[2-4]，P<0.001）和初级医生（4[3-4]，P<0.001）。CA-GPT在所有个体PCI前指标上均优于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）方面优于初级医生。在PCI后评估中，CA-GPT保持了极佳的整体一致性（5[4.75-5]），显著高于ChatGPT-5（4[4-5]，P<0.001）和初级医生（5[4-5]，P<0.05）。亚组分析确认CA-GPT在复杂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规划和评估阶段均优于通用大语言模型和初级医生，提供了标准化和可靠的血管内成像解释方法，展示了显著增强操作者专业知识和优化OCT引导PCI的潜力。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。