[2606.07721] Automatic Extraction of Structured Information from Brain MRI Reports Using an Open-Weight Large Language Model
[Submitted on 5 Jun 2026]
Title:Automatic Extraction of Structured Information from Brain MRI Reports Using an Open-Weight Large Language Model
View a PDF of the paper titled Automatic Extraction of Structured Information from Brain MRI Reports Using an Open-Weight Large Language Model, by Kaouther Mouheb and 11 other authors
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Abstract:Objectives: Automatic data extraction from free-text radiology reports enables large-scale research, but few studies assessed the performance of large language models (LLMs) on Dutch neuroradiology reports. Methods: We analyzed 947 brain MRI reports from a tertiary memory clinic (2016-2021), authored by consultant neuroradiologists. Trained medical students annotated thirty variables; 100 reports were double-annotated to assess inter-rater reliability. We evaluated the performance of the open-weight LLM LLaMA 3.1 using different languages (Dutch vs. English translation) and few-shot prompting with different example selection strategies. Performance was evaluated using balanced accuracy for categorical variables, accuracy and mean absolute error for counts, and text similarity for free-text. Metrics were computed across 10 random splits of the 947 reports. Results: LLaMA 3.1 demonstrated high zero-shot performance for visual rating scores (mean [95%-CI]): Medial Temporal Atrophy: 90% [77-100%] on the left and 96% [94-99%] on the right, Global Cortical Atrophy: 87% [83-91%], and Fazekas: 94% [93-96%]. Microbleed mentions were detected with 93% accuracy [92-95%] and infarct mentions with 82% [80-84%]. Text similarity for lesion location reached 0.95 [0.95-0.96]. Performance was lower for numerical variables: 80% [78-82%] for the number of microbleeds and 66% [63-68%] for infarcts. English translation yielded comparable results. Few-shot prompting improved performance for numerical variables, achieving 92% [90-93%] for microbleeds and 81% [77-85%] for infarcts using structural similarity-based selection. Conclusion: LLaMA 3.1 shows strong potential for extracting data from Dutch neuroradiology reports. Few-shot prompting enhances performance for numerical variables, whereas challenges remain for location-specific variables.
Comments: Submitted to European Radiology
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.07721 [cs.AI]
(or arXiv:2606.07721v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.07721
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Kaouther Mouheb [view email] [v1] Fri, 5 Jun 2026 15:57:35 UTC (6,056 KB)
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