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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.10702 |
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| _version_ | 1866908705571733504 |
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| author | Fang, Wei Wang, Chiyao Ma, Wenshuai Liu, Hui Hu, Jianqiang Niu, Xiaona Chu, Yi Zhang, Mingming Yang, Jingxiao Zhang, Dongwei Li, Zelin Liu, Pengyun Zheng, Jiawei Zhang, Pengke Qin, Chaoshi Guo, Wangang Wang, Bin Xue, Yugang Zhang, Wei Wang, Zikuan Zhu, Rui Cao, Yihui Lu, Quanmao Meng, Rui Li, Yan |
| author_facet | Fang, Wei Wang, Chiyao Ma, Wenshuai Liu, Hui Hu, Jianqiang Niu, Xiaona Chu, Yi Zhang, Mingming Yang, Jingxiao Zhang, Dongwei Li, Zelin Liu, Pengyun Zheng, Jiawei Zhang, Pengke Qin, Chaoshi Guo, Wangang Wang, Bin Xue, Yugang Zhang, Wei Wang, Zikuan Zhu, Rui Cao, Yihui Lu, Quanmao Meng, Rui Li, Yan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10702 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 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 Fang, Wei Wang, Chiyao Ma, Wenshuai Liu, Hui Hu, Jianqiang Niu, Xiaona Chu, Yi Zhang, Mingming Yang, Jingxiao Zhang, Dongwei Li, Zelin Liu, Pengyun Zheng, Jiawei Zhang, Pengke Qin, Chaoshi Guo, Wangang Wang, Bin Xue, Yugang Zhang, Wei Wang, Zikuan Zhu, Rui Cao, Yihui Lu, Quanmao Meng, Rui Li, Yan Artificial Intelligence 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. |
| 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 |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.10702 |