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Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative

Arun Nair1, Wilson Ong1, Aric Lee1

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Artificial intelligence (AI) can enhance magnetic resonance imaging (MRI) workflows by reducing scan and reading times and automating segmentation. Further validation is needed for widespread clinical adoption of these AI tools.

Keywords:
MRIartificial intelligenceautomated segmentationdeep learningmachine learningproductivityradiologist efficiencyradiology workflowworklist triage

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Magnetic resonance imaging (MRI) is widely used clinically.
  • Evaluating AI's impact on MRI productivity is limited.
  • AI offers potential to improve MRI efficiency and accuracy.

Purpose of the Study:

  • To systematically review AI's role in enhancing MRI productivity.
  • To synthesize evidence on AI's impact on scanning, reading, triage, and segmentation.
  • To identify common AI techniques and outcomes in MRI.

Main Methods:

  • Comprehensive literature search across multiple databases (PubMed, EMBASE, etc.) up to November 15, 2024.
  • Inclusion of 67 studies focusing on AI in MRI and productivity outcomes.
  • Categorization of studies into themes: scan time reduction, segmentation automation, workflow optimization, reading time decrease, and general workload reduction.

Main Results:

  • Convolutional neural networks (CNNs) like ResNet and U-Net are prevalent AI techniques.
  • AI applications demonstrated improvements in efficiency and diagnostic accuracy.
  • Common AI applications include segmentation, automated reporting, and workflow optimization.

Conclusions:

  • AI holds significant potential to boost radiologist productivity in MRI.
  • Key benefits include accelerated scans, automated segmentation, and streamlined workflows.
  • Further research on external validation and standardized metrics is crucial for clinical deployment.