ArticleInsights into imaging2019
What the radiologist should know about artificial intelligence - an ESR white paper.
Article in Insights into imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 135 papers, 4 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
135 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial intelligence for medical imaging education: bibliometric and visual analysis.Frontiers in medicine · 2026Pooled it
- Effectiveness of AI for Enhancing Computed Tomography Image Quality and Radiation Protection in Radiology: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis.European radiology · 2020Pooled it
- Promises of artificial intelligence in neuroradiology: a systematic technographic review.Neuroradiology · 2020Pooled it
- Cross-cultural adaptation and psychometric evaluation of the Turkish ATRAI-14 among radiology residents: a multicenter national survey.BMC medical education · 2026Article
- Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- Perceived communication quality of a standardized neuroradiology report following AI-based simplification: a randomized, blinded study.Insights into imaging · 2026Article
- Current challenges for global equity related to the implementation of artificial intelligence in pediatric imaging.Pediatric radiology · 2026Review
- Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.Journal of imaging · 2026Review
- Towards an accessible, centralised, searchable database for AI courses in Europe: the Artificial Intelligence in Medical Imaging and Radiation Oncology Education (AIMIROE) project.European radiology experimental · 2026Article
- Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Exams by Image: Ethical Pros and Cons.Healthcare (Basel, Switzerland) · 2026Review
- Artificial superintelligence alignment in healthcare.Japanese journal of radiology · 2026Review
- Comparative performance of four large language models in generating evidence-based exercise prescriptions using FITT-VP framework.Frontiers in physiology · 2026Article
- Development and evaluation of an AI model for dental implant type detection: A comparison of diagnostic accuracy between a deep learning model and dental professionals.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2026Article
- AI-Enabled Precision Nutrition in the ICU: A Narrative Review and Implementation Roadmap.Nutrients · 2025Review
- Comparative Evaluation of Large Language Models in Explaining Radiology Reports: Expert Assessment of Readability, Understandability, and Communication Features.Insights into imaging · 2025Article
- Radiomics in Action: Multimodal Synergies for Imaging Biomarkers.Bioengineering (Basel, Switzerland) · 2025Review
- Pulmonary Embolism Detection in Unenhanced CT Exams: A Radiomic-Based Classifier as a Promising Screening-Diagnostic Tool.Journal of personalized medicine · 2025Article
- Artificial Intelligence in Prostate MRI: Current Evidence and Clinical Translation Challenges-A Narrative Review.Journal of imaging · 2025Review
75 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper aims to provide a review of the basis for application of AI in radiology, to discuss the immediate ethical and professional impact in radiology, and to consider possible future evolution.Even if AI does add significant value to image interpretation, there are implications outside the traditional radiology activities of lesion detection and characterisation. In radiomics, AI can foster the analysis of the features and help in the correlation with other omics data. Imaging biobanks would become a necessary infrastructure to organise and share the image data from which AI models can be trained. AI can be used as an optimising tool to assist the technologist and radiologist in choosing a personalised patient's protocol, tracking the patient's dose parameters, providing an estimate of the radiation risks. AI can also aid the reporting workflow and help the linking between words, images, and quantitative data. Finally, AI coupled with CDS can improve the decision process and thereby optimise clinical and radiological workflow.
Indexed as
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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.