Automatic Extraction of Structured Information from Brain MRI Reports Using an Open-Weight Large Language Model
arXiv:2606.07721v1 Announce Type: new 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
The proliferation of open-weight LLMs like LLaMA 3.1 creates new opportunities for domain-specific applications, allowing for specialized data extraction that was previously less feasible or proprietary.
This development enables significant acceleration in medical research by automating the extraction of structured data from vast quantities of unstructured clinical reports, reducing manual effort and improving data scalability.
The ability to reliably extract complex medical information from free-text reports using open-weight LLMs changes how medical data is processed for research, potentially decentralizing AI development in healthcare.
- · Medical Researchers
- · Open-source LLM developers
- · Healthcare AI platforms
- · Hospitals/Clinics
- · Manual data annotation services
- · Proprietary medical NLP solutions (less competitive)
- · Traditional medical data entry roles
Research in neurological conditions can be significantly accelerated due to readily available structured data from historical patient reports.
The improved accessibility of clinical data could lead to new diagnostic tools and treatment protocols based on large-scale analysis.
This could democratize advanced medical AI capabilities, allowing smaller institutions or countries to leverage sophisticated data analysis tools without relying on expensive commercial solutions.
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Read at arXiv cs.AI