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Artificial Intelligence for CT and MRI Protocoling: A Meta-Analysis of Traditional Machine Learning, BERT, and Large

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Artificial intelligence (AI) models, including traditional machine learning (ML), BERT, and large language models (LLMs), show promise for automating CT and MRI examination protocoling. Fine-tuned BERT models demonstrated the highest performance in a meta-analysis of 23 studies.

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Machine Learning Applications

Background:

  • Examination protocoling for CT and MRI is a labor-intensive process.
  • Artificial intelligence (AI) offers potential solutions for automating protocoling tasks.

Purpose of the Study:

  • To evaluate the performance of traditional machine learning (ML) models, BERT models, and large language models (LLMs) for automated CT and MRI protocoling.
  • To compare the accuracy of different AI approaches in assigning imaging protocols.

Main Methods:

  • A systematic literature search was conducted across multiple databases (MEDLINE, Embase, Scopus, Web of Science, IEEE Xplore, Google Scholar) up to July 2025.
  • Included studies reported performance metrics for AI-based CT/MRI protocoling techniques.
  • Random-effects meta-analysis was used to pool accuracy results, with Welch t tests for comparisons.

Main Results:

  • The analysis included 23 studies covering over 1.19 million imaging requisitions.
  • Overall pooled accuracy for AI protocoling was 85%. Pooled accuracies for traditional ML, BERT, and LLMs were 83%, 87%, and 86%, respectively, with no significant differences between AI types.
  • Fine-tuned BERT models, particularly BioBert, achieved the highest accuracy (93%), with six of the top ten performing models being BERT-based.

Conclusions:

  • Fine-tuned BERT models represent the leading AI approach for automated CT and MRI protocoling.
  • AI tools hold significant potential for streamlining radiologist workflows, possibly via hybrid AI-radiologist collaboration.
  • Further research into fine-tuned LLMs for this application is warranted.