ReviewSaudi medical journal2026
Artificial Intelligence in Radiology: Hidden Fragilities and the Path to Resilience.
Review in Saudi medical journal, 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiology artificial intelligence (AI) is advancing however its adoption faces fragile foundations that threaten sustainability. Despite bold promises of efficiency and accuracy, current deployment is undermined by weaknesses in economics, evidence, infrastructure, human factors, regulation, security, and environmental impact. Nearly 90% of radiology AI studies report process metrics rather than patient outcomes, while hidden costs elevate ownership to 400% to 500% of subscription fees. Technical fragilities include 25% or greater performance loss with routine protocol or scanner shifts, compounded by vendor consolidation that has eliminated 63% of companies since 2020, creating migration costs averaging 180,000 dollars per exit. Human factor challenges, including automation bias and progressive deskilling, intersect with regulatory requirements that mandate continuous evidence generation. Security risks and environmental costs remain underrecognized. This review introduces frameworks including risk assessment matrices, compliance guides, procurement checklists, evidence standards, lifecycle calculators, and implementation protocols to enable sustainable, patient centered, value driven integration.
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Identifiers
What OpenQuestion holds
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.