ReviewEuropean radiology2026
Radiologist burnout: AI's true black box.
Review in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Maintaining the balance: wellbeing strategies for abdominal radiologists.Abdominal radiology (New York) · 2026Review
- Reply to the Letter to the Editor: Blind spots in radiology leadership regarding human resources and organization management in the era of artificial intelligence.European radiology · 2026Article
- Between automation and alienation: rethinking AI's role in radiologist well-being.European radiology · 2026Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Multiple articles have touted the longitudinal promise of artificial intelligence (AI) in radiology, including projections of streamlining repetitive tasks, improving workflow, and reducing physician burnout. The purpose of this article is to review publications directly assessing the impact of AI on radiologist burnout and the impact of AI on the established drivers of radiologist burnout. Our analysis found conflicting, inconclusive limited data that AI reduces radiologist burnout, and the balance of data does not support that AI improves the drivers of burnout. How AI affects radiologist burnout remains a "black box", with the final impact yet to be determined. KEY POINTS: Question While AI has been touted to reduce radiologist burnout, the literature to date supporting this claim has not been explored. Findings Our analysis found inconclusive, limited data that AI reduces radiologist burnout, and that the balance of data does not support that AI improves the drivers of burnout. Clinical relevance Despite the optimism towards AI implementation in radiology, how AI truly affects radiologist burnout remains a "black box", with the final impact yet to be determined.
Indexed as
Identifiers
What Socratic 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 Socratic graph.