ReviewDiagnostics (Basel, Switzerland)2026
Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.
Review in Diagnostics (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiology services are experiencing increasing operational complexity due to rising imaging volumes and expanding coordination demands across interconnected clinical and administrative processes. This complexity is reflected in variability across workflow stages, driven by fragmented information flows, heterogeneous system integration, and multi-source data dependencies. Artificial intelligence (AI) has therefore emerged as a potential tool to support automation, prioritization, and operational efficiency throughout the radiology pathway. This narrative review examines published applications of AI within a nine-stage representation of the radiology workflow. The review synthesizes how AI methods are being investigated to support both administrative coordination and diagnostic processes in radiology practice. AI approaches aim to reduce repetitive administrative tasks, improve resource utilization, and assist radiologists in managing increasing imaging workloads. However, research activity remains uneven, with a strong concentration on later-stage tasks such as image analysis and reporting, while earlier and administrative stages remain comparatively underexplored. By organizing existing research within a unified workflow-oriented framework, this review highlights areas of concentration and identifies gaps across less-studied stages. The findings suggest that while several AI applications are approaching early clinical deployment, broader workflow-level impact remains limited by challenges related to system integration, interoperability, governance, and real world implementation. Continued progress will depend on developing integrated and clinically validated solutions that extend beyond isolated tasks to support coordinated radiology workflow optimization.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.