Background: Microsurgical decision-making requires integration of diverse patient-specific factors, advanced surgical techniques, and dynamic intraoperative insights. While artificial intelligence (AI), large language models (LLMs), and retrieval-augmented generation (RAG) models have advanced significantly in various fields, no AI-driven clinical decision support systems currently exist for microsurgery. We developed MicroRAG, the first AI-powered clinical decision support system specifically designed for microsurgery, capable of instantly providing evidence-based recommendations by searching and synthesizing the entire microsurgical literature.
Methods: We developed an AI clinical decision support system integrating 4876 peer-reviewed microsurgical publications (2000-2024) using advanced retrieval-augmented generation (RAG) technology. The system processes clinical queries through hierarchical document clustering and provides real-time, evidence-based recommendations with direct literature citations. We evaluated system performance using 10 standardized clinical scenarios covering common microsurgical decisions, measuring answer relevancy, faithfulness to source literature, and clinical accuracy.
Results: MicroRAG demonstrated exceptional performance with an average answer relevancy score of 0.953 (range: 0.857-1.000) and faithfulness score of 0.907 (range: 0.676-1.000). G-Eval correctness averaged 0.88 with Semantic Evaluation Metrics showing an average similarity score of 0.75 and confidence score of 0.80. The system successfully provided comprehensive, immediately actionable guidance for complex scenarios including free flap monitoring protocols, vascular complication management, and surgical technique selection. All responses were grounded in peer-reviewed literature with direct citations.
Conclusion: MicroRAG represents a technological innovation in microsurgical practice, providing instant access to evidence-based recommendations that typically require hours of literature review. By delivering comprehensive, literature-grounded guidance in real-time, this system has the potential to standardize best practices, reduce decision-making uncertainty, and ultimately improve patient outcomes across all levels of surgical experience.
Keywords: artificial intelligence; clinical decision support; large language models; microsurgery; retrieval‐augmented generation.
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