ReviewCVIR endovascular2025
Clinical utility of artificial intelligence models in radiology: a systemic scoping review of diagnostic and endovascular applications.
Review in CVIR endovascular, 2025. 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.
- Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.Journal of imaging · 2026Review
- Artificial Intelligence in the Diagnosis and Prognostic Stratification of Hepatocellular Carcinoma: Current Evidence, Clinical Applications, and Future Perspectives.Biomedicines · 2026Review
- Blind spots in artificial intelligence systems: poor identification of self-generated medical images-evidence-based cross-sectional study.Frontiers in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo systematically scope the clinical integration of artificial intelligence (AI) in diagnostic and interventional radiology. This integration encompasses various components of AI forms such as deep learning, convolutional neural networks, natural language processing, and machine learning. METHODOLOGY: A Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) was employed to evaluate current primary and translation literature on the utility of AI in diagnostic and interventional radiology in broad disease categories.
resultsFollowing the review for inclusion criteria, a total of 23 peer-reviewed research articles were selected for review. Notably, most studies were found to focus on diagnostic and interventional radiology and oncologic diseases, including lung, hepatocellular, colorectal, prostate, pancreatic, breast, and blood cancers.
conclusionsRadiologists have an advantageous role with the integration of these tools in clinical practice. This may include disease prediction models, catheter navigation, and image reconstruction. Utilization of these AI tools can help improve and further expose of the capabilities of diagnostic and interventional radiology to patients worldwide. From a disease standpoint, this review found most of the clinical literature has implemented AI tools for diagnostic and interventional radiology in oncology, followed by vascular diseases. Careful navigation is necessary to address the current logistical challenges, educational demands, and ethical dilemmas to ensure the safe and effective incorporation of these technologies into clinical radiologic settings.
Indexed as
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.