Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Oct 21, 2025
Date Accepted: May 31, 2026
Automating the Management of Extraspinal Findings in MRI Spine Studies Using a Privacy-Preserving Large Language Model: A Single-Institution Feasibility Study
ABSTRACT
Background:
MRI spine studies frequently reveal extra-spinal findings (ESFs) that require further evaluation, yet the current process of manually reviewing radiology reports and navigating electronic medical records (EMRs) is time-consuming, labour-intensive and prone to human error.
Objective:
To address this challenge, we propose using a privacy-preserving large language model (PP-LLM) to automate the identification, classification, and referral assessment of extra-spinal findings (ESFs).
Methods:
A retrospective analysis of 400 consecutive MRI spine reports from the National University Hospital (NUH) database, covering February to June 2024, was conducted. Two independent clinicians reviewed the reports and cross-referenced them with EMRs to identify ESFs from the imaging reports. The CT Extracolonic Findings Reporting and Data System (C-RADS system), was adapted to determine the clinical significance of ESFs and whether specialty referral was required. The PP-LLM was designed to extract these findings, differentiate between new and pre-existing conditions, classify their clinical significance, and generate appropriate referrals.
Results:
A retrospective analysis of 400 consecutive MRI spine reports from the National University Hospital (NUH) database, covering February to June 2024, was conducted. Two independent clinicians reviewed the reports and cross-referenced them with EMRs to identify ESFs from the imaging reports. The CT Extracolonic Findings Reporting and Data System (C-RADS system), was adapted to determine the clinical significance of ESFs and whether specialty referral was required. The PP-LLM was designed to extract these findings, differentiate between new and pre-existing conditions, classify their clinical significance, and generate appropriate referrals.
Conclusions:
The PP-LLM demonstrated high accuracy and efficiency in automating the identification and classification of extra-spinal findings in MRI spine reports. By integrating this AI-driven automation into clinical workflows, this technology has the potential to enhance efficiency, reduce clinician administrative burden, ensure timely specialist referrals and improve patient care. Clinical Trial: NA
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