SynthesisEuropean radiology2026
Cost-effectiveness of artificial intelligence tools in radiology: a systematic review.
Synthesis in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- A Decade of Artificial Intelligence in Stroke Care (2015-2025): Trends, Clinical Translation, and the Precision Medicine Frontier-A Narrative Review.Journal of personalized medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo systematically review the evidence on the cost-effectiveness of artificial intelligence (AI) interventions for diagnostic imaging in radiology, thereby identifying methodological gaps and priorities for future research. MATERIALS AND
methodsA PRISMA-compliant search of PubMed, Cochrane Library, Scopus, Web of Science, and Embase was conducted for English-language studies published until January 23, 2025. Eligible studies evaluated AI-based radiology interventions for diagnostic imaging with formal economic analysis. Studies were appraised using the Consolidated Health Economic Evaluation Reporting Standards for Artificial Intelligence-based Interventions (CHEERS-AI) checklist.
resultsOf 360 publications identified, ten met the inclusion criteria. Nine studies reported cost-utility analyses using quality-adjusted life-years (QALYs) and one used disability-adjusted life-years (DALYs). All studies employed theoretical modeling (Markov, decision-tree, or hybrid simulations), with no prospective real-world cost-effectiveness data. Applications included cancer screening, acute stroke detection, infection control, and opportunistic detection of incidental findings. Most included studies used publicly available healthcare data from the United States or the United Kingdom to model cost-effectiveness outcomes, and concluded that AI may be cost-effective under model-specific assumptions and willingness-to-pay thresholds. Methodological heterogeneity precluded meta-analysis.
conclusionCurrent evidence on the cost-effectiveness of AI in radiology is limited, model-based, and lacks validation with real-world prospective data. Future research should employ standardized evaluation frameworks and incorporate empirical clinical data to better inform implementation decisions. KEY POINTS: Question Economic evaluations are important for sustained implementation of AI in radiology, but current evidence on cost-effectiveness is limited. Findings Ten studies, predominantly from the United States and the United Kingdom, used model-based analyses and generally indicated cost-effectiveness under study-specific assumptions, without prospective real-world cost data. Clinical relevance Despite increasing adoption of AI tools in radiology, current cost-effectiveness evidence is limited, wholly model-based, and methodologically heterogeneous. Standardized economic evaluations using prospective real-world data are needed to support informed adoption decisions.
Indexed as
Identifiers
41441999What 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.