Concordance Between Clinical Practice Recommendations Generated by Generative Artificial Intelligence and the Vía RICA 2026 Enhanced Recovery Guideline: A Proof-of-Concept Study on a Closed Corpus of Primary Evidence

Clinical practice guidelines such as the Spanish Enhanced Recovery After Surgery protocol Vía RICA require intensive expert synthesis of primary evidence that large language models (LLMs) might partly automate, yet their ability to reproduce clinically actionable recommendations remains poorly quantified. We quantitatively evaluate an LLM (Claude Sonnet 4.6) generating recommendations against the 103 expert-curated recommendations of Vía RICA 2026, organised in 17 clinical bundles. The model used the panel's own closed corpus (617 articles, 36,054 chunks) through a retrieval-augmented generation pipeline with multilingual embeddings. Concordance was assessed under a dual framework: optimal 1:1 bipartite matching (Hungarian) on cosine similarity, and an LLM-as-a-judge clinical adjudicator (Claude Sonet 4.6)—whose agreement with two independent clinicians (Cohen's \( \kappa \)= 0.57 and 0.88) fell within the range of human inter-rater variability (human--human \( \kappa \) = 0.64)—over 499 borderline pairs. The two schemes bracket a micro F1 of 0.61–0.69 and reveal three findings: (i) a systematic granularity bias, the LLM producing 3–8 recommendations per bundle regardless of ground-truth size; (ii) failure of cosine similarity to discriminate within narrow clinical domains; and (iii) high factual precision (0.70–0.81) despite low structural exhaustiveness. The results delimit the current utility of generative AI for guideline elaboration.