What question did this study set out to answer?

The study aims to evaluate the use of secure large language models (sLLMs) in enhancing the accuracy and context of MRI examination requests.

April 10, 2026Open Access

Initial Insights Into an Institutional Secure Large Language Model for Magnetic Resonance Imaging Examination Requests: Retrospective Study

Key Points

The study aims to evaluate the use of secure large language models (sLLMs) in enhancing the accuracy and context of MRI examination requests.
Conducted a retrospective analysis across various MRI types—body, musculoskeletal, and neuroradiology.
Implemented sLLMs to augment examination requests.
Assessed the accuracy of sLLM-enhanced requests compared to general radiologists.
sLLM integration improved clinical context and contrast selection in MRI requests.
Demonstrated accuracy comparable to general radiologists regarding region or coverage.
Potential reduction in manual workload for protocol selection was noted.

Abstract

Across body, musculoskeletal, and neuroradiology MRI, sLLM-augmented examination requests improved clinical context and enhanced contrast selection while demonstrating accuracy comparable to general radiologists for region or coverage. Integrating sLLMs into routine vetting workflows may reduce manual workload in protocol selection for more efficient, standardized protocoling.

Initial Insights Into an Institutional Secure Large Language Model for Magnetic Resonance Imaging Examination Requests: Retrospective Study

Key Points

Abstract

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