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
🤖 AI Summary
Optical coherence tomography (OCT)-guided percutaneous coronary intervention (PCI) heavily relies on operator expertise, and general-purpose AI models lack domain-specific reliability for cardiovascular interventions.
Method: We propose CA-GPT—the first large language model specifically designed for OCT-PCI—integrating a modular clinical decision architecture with a medical retrieval-augmented generation (RAG) mechanism to support end-to-end tasks: semantic parsing of coronary anatomy, pre-procedural planning (lumen diameter/length measurement), and post-procedural assessment (stent apposition, expansion, etc.).
Results: In real-world clinical validation, CA-GPT achieved inter-rater agreement rates of 90.3% (diameter) and 80.6% (length) with expert cardiologists—significantly outperforming ChatGPT-4o and junior physicians (p<0.01). For post-procedural evaluation, it attained perfect median consistency (5/5; IQR: 4.75–5) and demonstrated robust performance in complex cases. This study provides the first clinical validation of domain-specialized LLMs for OCT-PCI, establishing their feasibility and superiority over generalist models and novice practitioners.
Application Category
📝 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<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.
Problem
Research questions and friction points this paper is trying to address.
Improving operator-dependent interpretation of intravascular OCT imaging for PCI
Addressing lack of domain-specific reliability in general-purpose AI for medical decisions
Providing standardized decision support for both pre-PCI planning and post-PCI assessment
Innovation
Methods, ideas, or system contributions that make the work stand out.
CA-GPT AI-OCT system uses RAG-enhanced large model for PCI
It outperforms ChatGPT-5 and junior physicians in planning
Provides standardized OCT interpretation for superior decision support
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