SPJ Publication
The Radpac
Open Access Short Communication

AI Applications in Workflow Optimization in Radiology Departments

*

* Corresponding author: Dr. Sumit Agrawal

DOI10.62502/tr/v1i2ar.5
Pages 20–23Language ENLicenceCC BY-NC 4.0 Crossmark XML metadata
  1. Received18 Aug 2025
  2. Accepted09 Nov 2025
  3. Published27 Dec 2025

Abstract

Radiology departments across the world are experiencing unprecedented operational pressure due to rapidly increasing imaging volumes, growing clinical complexity, and a persistent shortage of trained radiologists. These challenges have resulted in prolonged reporting turnaround times, workflow inefficiencies, and increased professional burnout. Artificial intelligence (AI) has emerged as a powerful adjunct technology capable of supporting radiology workflows by automating repetitive tasks such as image triage, abnormality detection, reporting assistance, and quality assurance. This review synthesizes evidence published between 2020 and 2026 to evaluate the role of AI in optimizing radiology workflows across major imaging modalities, including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), and radiotherapy planning. Recent studies demonstrate that AI-assisted workflows can reduce turnaround time by 30–50%, improve prioritization of critical findings, and enhance overall departmental efficiency without compromising diagnostic accuracy. However, challenges related to algorithm transparency, data bias, regulatory variability, and implementation costs remain barriers to widespread adoption. This review discusses current applications, supporting evidence, limitations, and future directions, providing practical insights for radiology departments seeking to integrate AI into routine clinical practice

Keywords

Artificial IntelligenceRadiology WorkflowTurnaround TimePACS

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