ArticleCardiovascular and interventional radiology2026
Retrieval-Augmented Language Models for Patient-Centered Periprocedural Anticoagulation in Interventional Radiology.
Article in Cardiovascular and interventional radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Evaluation Methods for Inference-Time Retrieval-Augmented and Graph Retrieval-Augmented Large Language Models in Health Care: Scoping Review.Journal of medical Internet research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study evaluates Large Language Models (LLMs) integrated with Retrieval-Augmented Generation (RAG) frameworks for generating accurate, guideline-concordant anticoagulation (AC) recommendations in Interventional Radiology (IR) MATERIALS AND
methods394 IR procedure-AC pairs were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Five state-of-the-art LLMs, Claude-3.5-Sonnet, GPT-4o, LLaMA-3.1, Mistral-Large-2411, and Qwen-2.5, were deployed within a RAG system built using LlamaIndex, which indexed the Society of Interventional Radiology (SIR) guidelines. Claude was evaluated without RAG integration (Base Claude). Models were evaluated on withholding AC, withholding and reinitiation timeframes, and platelet and International Normalized Ratio (INR) thresholds. A human assessor scored outputs for guideline concordance (2 = complete, 1 = partial, 0 = none), which were subsequently averaged.
resultsClaude achieved the highest overall guideline concordance (1.51/2). Claude, GPT-4o, and Mistral performed similarly in withholding decisions (1.88 [95% CI 1.83, 1.93], 1.86 [1.81, 1.91], and 1.88 [1.84, 1.93]). Claude and GPT-4o showed comparable accuracy in both withholding (1.56 [1.44, 1.67] vs. 1.55 [1.44, 1.67]; p=0.871) and reinitiation timeframes (1.34 [1.20, 1.47] vs. 1.37 [1.26, 1.49]; p=0.920). Mistral achieved the highest platelet threshold concordance (1.52 [1.45, 1.59]), while Claude led in International Normalized Ratio (INR) recommendations (1.37 [1.32, 1.42]). LLaMA (0.76) and Qwen (0.52) significantly underperformed across all categories (p<0.001). RAG integration significantly improved Claude's performance across all categories, except platelet thresholds.
conclusionLLMs integrated with RAG systems, especially Claude, GPT-4o, and Mistral, demonstrate potential for delivering guideline-concordant, patient-specific AC recommendations in IR, supporting their role as safe, efficient, and customizable clinical decision-support tools.
Indexed as
Identifiers
41917168What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.