What question did this study set out to answer?

To evaluate the diagnostic accuracy, operational integration, and workflow impact of artificial intelligence models designed for automated MRI and CT protocol selection.

January 24, 2026European Journal of Radiology Artificial IntelligenceOpen Access

Artificial intelligence in radiology workflow: A systematic review into protocol automation and clinical applications

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To evaluate the diagnostic accuracy, operational integration, and workflow impact of artificial intelligence models designed for automated MRI and CT protocol selection.
Systematically searched PubMed, Embase, Scopus, and Medline between January 2000 and February 2025 for original studies applying NLP, machine learning, or deep learning to radiology protocol assignment.
Screened 742 articles with two independent reviewers using Rayyan, assessed risk of bias via QUADAS-2, and synthesized findings narratively across 15 included studies.
Transformer-based NLP models achieved 84%–98% accuracy for MRI protocol selection, while CT protocols achieved ~85% accuracy in multiclass machine learning and ~99% concordance in institutional rule-based models.
Machine learning models such as random forest and support vector machines exceeded 83% accuracy, reducing protocoling time by up to 72% and automating 60%–70% of outpatient referral workflows.

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Implication

Systematic review demonstrates high accuracy and reduced ordering time for automated imaging referrals, indicating significant workflow benefits in clinical radiology.

Cite This Study

Hadi et al. (2026) studied this question.

synapsesocial.com/papers/6a1bd27dbc71fb1015a9013a https://doi.org/10.1016/j.ejrai.2026.100073

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Implication

Systematic review demonstrates high accuracy and reduced ordering time for automated imaging referrals, indicating significant workflow benefits in clinical radiology.

Artificial intelligence in radiology workflow: A systematic review into protocol automation and clinical applications

Key Points

Discussion

Implication

Cite This Study

Artificial intelligence in radiology workflow: A systematic review into protocol automation and clinical applications

Key Points

Discussion

Implication

Cite This Study

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