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Initial Insights Into an Institutional Secure Large Language Model for Magnetic Resonance Imaging Examination

James Thomas Patrick Decourcy Hallinan1,2, Naomi Wenxin Leow3, Yi Xian Low1

  • 1Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore, 65 6908 2222.

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Summary
This summary is machine-generated.

A secure large language model (sLLM) improved magnetic resonance imaging (MRI) examination requests by enhancing clinical details and protocol accuracy. This AI integration shows comparable or superior performance to radiologists, streamlining workflows.

Keywords:
body imagingmagnetic resonance imagingmusculoskeletal imagingneuroradiology imagingradiology request formreason for exam imaging reporting and data systemsecure large language model

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support Systems

Background:

  • Incomplete clinical details in magnetic resonance imaging (MRI) examination requests (MERs) can compromise protocol selection.
  • A secure large language model (sLLM) accessing electronic medical record (EMR) data may enhance MER completeness and accuracy across MRI subspecialties.

Purpose of the Study:

  • To compare the information quality of clinician-generated MERs versus sLLM-augmented MERs.
  • To evaluate the protocoling accuracy of an sLLM against board-certified radiologists in body, musculoskeletal, and neuroradiology MRI.

Main Methods:

  • Retrospective analysis of 608 outpatient MRI examinations (Sep 2023 - Jul 2024).
  • sLLM augmented MERs with manually retrieved EMR data for rule-based criteria mapping.
  • Comparison of sLLM and radiologist protocoling accuracy against a consensus reference standard using RI-RADS and Gwet AC1 metrics.

Main Results:

  • sLLM-augmented MERs showed almost perfect interreader agreement (AC1 0.97) versus moderate for clinician MERs (AC1 0.43).
  • Deficient clinical information rates dropped from 4.1%-20.4% to 0%-0.7% with sLLM augmentation.
  • Overall protocol accuracy was comparable (sLLM: 93.1%, Radiologists: 91.4%-92.1%), with the sLLM demonstrating superior contrast decision accuracy (94.4%).

Conclusions:

  • sLLM-augmented MRI requests improve clinical context and contrast selection accuracy, comparable to general radiologists.
  • Integrating sLLMs into vetting workflows can reduce manual workload, leading to more efficient and standardized MRI protocoling.