{"ID":2830300,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.10702","arxiv_id":"2512.10702","title":"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","abstract":"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\u003c0.001) and junior physicians (4[3-4], P\u003c0.001). 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\u003c0.05) and length selection (80.6% vs. 52.8%, P\u003c0.01). In post-PCI assessment, CA-GPT maintained excellent overall agreement (5[4.75-5]), significantly higher than both ChatGPT-5 (4[4-5], P\u003c0.001) and junior physicians (5[4-5], P\u003c0.05). 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.","short_abstract":"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 o...","url_abs":"https://arxiv.org/abs/2512.10702","url_pdf":"https://arxiv.org/pdf/2512.10702v1","authors":"[\"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\"]","published":"2025-12-11T14:41:37Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Language Model\"]","has_code":false}