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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Linear accelerator (linac) downtime analysis assisted with a Large Language Model (LLM).

Yeochan Youn1,2, Joseph B Schulz3, Ethan Stolen2

  • 1Data Science Institute, University of Chicago, Chicago, Illinois, USA.

Journal of Applied Clinical Medical Physics
|November 27, 2025
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Summary
This summary is machine-generated.

This study analyzed linear accelerator (LINAC) maintenance records, finding consistent uptime and identifying key components needing repair. The findings offer insights for optimizing radiation therapy maintenance and budget management.

Keywords:
LLMdowntimelinear acceleratormachine performancetime series decomposition

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Biomedical Engineering

Background:

  • Linear accelerators (LINACs) are vital for radiation therapy, demanding high reliability for patient care.
  • Unplanned downtime in LINACs disrupts clinical workflows and can compromise treatment efficacy.
  • Traditional reliability analyses often overlook comprehensive performance patterns in favor of component-specific issues.

Purpose of the Study:

  • To analyze 10 years of maintenance records from Varian TrueBeam LINACs to understand long-term operational performance.
  • To identify common fault patterns, quantify downtime, pinpoint vulnerable components, and assess the pandemic's impact on maintenance.
  • To leverage artificial intelligence and large language models (LLMs) for analyzing unstructured maintenance data.

Main Methods:

  • Analyzed 1584 maintenance reports from three Varian TrueBeam LINACs over 4-13 years of operation.
  • Employed data normalization to standardize service documents and LLM-based custom prompt engineering for report classification (work and failure types).
  • Conducted descriptive analysis, including time series decomposition for seasonal trends and evaluation of external technician involvement.

Main Results:

  • LINACs maintained operational downtime within the agreed 5% threshold, with only one instance nearing the limit.
  • Collimation systems, control hardware, and power systems were the most frequent maintenance subjects; repair and replacement were common work types.
  • No age-related increase in failure frequency was observed; weak seasonality was detected. Field service engineer visits and post-2020 work hours increased, particularly for replacements.

Conclusions:

  • Findings offer valuable insights for clinical budget management and maintenance scheduling for LINACs.
  • The developed LLM-based data analysis methodology provides an effective approach for analyzing historical maintenance records.
  • This methodology can be adopted by other institutions to enhance LINAC operational performance and maintenance strategies.