ArticleEuropean radiology2020
Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 123 papers, 9 of them syntheses that pooled it.
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
123 citing papers in PubMed, 9 syntheses or guidelines pooled it, 231 citations in OpenAlex.
- Accuracy of Radiomics-Based Machine Learning for Predicting Risk of Recurrence in Non-Small Cell Lung Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Systematic review of artificial intelligence competitions in radiology: a focus on design, evaluation, and trends.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Pooled it
- Use of artificial intelligence in education and training of radiology.Frontiers in radiology · 2026Pooled it
- Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools.European radiology · 2025Pooled it
- A 25-Year Retrospective of the Use of AI for Diagnosing Acute Stroke: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Cardiac CT and MRI radiomics: systematic review of the literature and radiomics quality score assessment.European radiology · 2022Pooled it
- Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review.Neuroradiology · 2021Pooled it
- Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis.European radiology · 2021Pooled it
- Promises of artificial intelligence in neuroradiology: a systematic technographic review.Neuroradiology · 2020Pooled it
- Cultural bias in large language models' ability to follow neuroradiology guidelines.European radiology · 2026Article
- Performance, Heterogeneity, and Methodological Quality of YOLO-Based Models for Fracture Detection: A Systematic Review and Meta-Analysis.Journal of imaging informatics in medicine · 2026Review
- Diagnostic performance of large language models for discrepancy detection in non-English resident-authored abdominal CT and MRI reports: a Turkish-language evaluation.Abdominal radiology (New York) · 2026Article
- Clinical Comparison of Two Versions of a Commercial Artificial Intelligence System for Identifying Candidate Locations of Cerebral Aneurysms: Reduction and Characterization of False-Positive Findings.Journal of clinical medicine · 2026Article
- Artificial Intelligence for Cerebral Aneurysm Management: Integrating Imaging, Hemodynamics, and Clinical Decision Support.Journal of imaging informatics in medicine · 2026Review
- Methodological Pluralism in Surgical Evidence: from Concept to Practice.Surgery in practice and science · 2026Review
- AI Adoption in US Cancer Centers: National Cross-Sectional Study of Institutional and Policy Determinants.Journal of medical Internet research · 2026Article
- Temporal Reproducibility of Fracture Interpretation in Forensic Radiography: A Multispecialty Comparison of Physicians and Vision Language Models Including Fracture Subtype Description.Tomography (Ann Arbor, Mich.) · 2026Article
- Artificial intelligence in paediatric neuroradiology: current landscape, challenges, and future directions.Pediatric radiology · 2026Review
- Cloud-Enabled Automated CT Assessment of Pelvic Muscle Quality in Women With and Without Low-Energy Femoral Neck Fracture.Calcified tissue international · 2026Article
- Automated detection of lumbar disc herniation at L4-L5 and L5-S1 levels on sagittal MRI using a YOLO-based deep learning model.BMC musculoskeletal disorders · 2026Article
63 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 7 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has the potential to significantly disrupt the way radiology will be practiced in the near future, but several issues need to be resolved before AI can be widely implemented in daily practice. These include the role of the different stakeholders in the development of AI for imaging, the ethical development and use of AI in healthcare, the appropriate validation of each developed AI algorithm, the development of effective data sharing mechanisms, regulatory hurdles for the clearance of AI algorithms, and the development of AI educational resources for both practicing radiologists and radiology trainees. This paper details these issues and presents possible solutions based on discussions held at the 2019 meeting of the International Society for Strategic Studies in Radiology. KEY POINTS: • Radiologists should be aware of the different types of bias commonly encountered in AI studies, and understand their possible effects. • Methods for effective data sharing to train, validate, and test AI algorithms need to be developed. • It is essential for all radiologists to gain an understanding of the basic principles, potentials, and limits of AI.
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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.