ArticleClinical neuroradiology2026
Data-efficient Neuroradiology MRI Imaging Protocol Prediction Using Open-weights Large Language Models.
Article in Clinical neuroradiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
purposeTo evaluate the feasibility of a locally deployable large language model (LLM) system for automated MRI protocol selection addressing data privacy, annotation burden, and scalability limitations.
methodsThis retrospective study included 598 German-language MRI order entries from three neuroradiology domains (brain, head/neck, spine) between June 2018 and January 2023. A radiologist labeled entries for 27 protocol classes based on institutional standard operating procedures (SOP). An SOP-grounded AI system using MedGemma 27B was developed to predict the MRI protocol from the order entry. The system was optimized using Stochastic Introspective Mini-Batch Ascent (SIMBA), a self-reflective prompt optimization algorithm, and compared with a hierarchical system that first classified the body region and then the MRI protocol. Data efficiency was evaluated using training subsets of 10-119 examples across 3 optimization runs per subset size.
resultsThe flat zero-shot model achieved 73.07% accuracy in the three-domain setting on the held-out dataset (n = 479). In the hierarchical model, prompt optimization yielded 73.90% ± 3.24% with 30 labeled examples and a maximum of 74.46% ± 3.82% but did not outperform the flat approach. Prompt optimization benefited the hierarchical system, whereas the flat model already performed strongly without labeled examples for optimization. Performance on brain and head/neck cases remained broadly stable after expansion from 22 to 27 protocol classes, i.e., including spine.
conclusionA locally deployable SOP-grounded open-weight LLM can support MRI protocol selection while preserving data privacy and needing minimal labeled data. In this dataset, hierarchical routing and prompt optimization did not improve overall performance over the flat baseline, although they altered optimization behavior and error profile. These findings support prospective evaluation in human-in-the-loop clinical workflows.
Indexed as
Identifiers
42446636What 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.