A multi-guideline clinical agent for vascular surgery: architecture, implementation, and benchmark evaluation

Abstract

Abstract Background The clinical practice guidelines (CPGs) published by the European Society for Vascular Surgery (ESVS) span approximately 1100 pages whose simultaneous cross-referencing is impractical at the point of care. Unguarded large language models (LLMs) carry hallucination risk, motivating a guideline-locked design. Methods We describe the architecture and development of a context-aware clinical agent encompassing the full ESVS corpus of 14 CPGs. The system extends a validated single-guideline GraphRAG prototype with an LLM-driven Guideline Router, an embedded Context Gate that withholds retrieval when decision-critical parameters are absent, dual-channel retrieval, and GPT-5 synthesis under a mandatory citation template. An automated benchmark assessed the final system on 55 vignettes, scored by 2 blinded LLM judges over 3 runs (330 judgments), with clinician adjudication of every flagged claim. Results The system stayed within its locked corpus, declined all out-of-scope queries, and achieved 98.4% guideline-routing recall. The Context Gate showed 91.9% specificity and 78.6% sensitivity for underspecified queries, and all 259 cited recommendations were verifiable within the locked corpus. Automated judges flagged an unsupported claim in 18.2% of items under a sensitive screening definition; on adjudication by 2 vascular surgeons, 7.3% of items contained a clinically consequential error, none graded capable of causing harm. Clinical acceptability of complete answers was not scored and is reserved for prospective expert evaluation. Conclusion These results support the feasibility of a multi-guideline context-aware clinical agent for vascular surgery; citation traceability and locked-corpus provenance facilitate future auditability.