ArticleRadiology. Artificial intelligence2024
Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA.
Article in Radiology. Artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 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
30 citing papers in PubMed, 2 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- AI implementation in pediatric radiology for patient safety: a multi-society statement from the ACR, ESPR, SPR, SLARP, AOSPR, SPIN.Pediatric radiology · 2026Guideline
- [Applications of artificial intelligence in radiology].Radiologie (Heidelberg, Germany) · 2024Pooled it
- Artificial Intelligence Reporting Guidelines in Radiology: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Reply to the Letter to the Editor: Automated deep learning model for the accurate detection and measurement of adrenal masses in contrast-enhanced abdominal CT.European radiology · 2026Article
- Current challenges for global equity related to the implementation of artificial intelligence in pediatric imaging.Pediatric radiology · 2026Review
- Independent bone-level diagnostic accuracy study of an AI tool for detecting appendicular skeletal fractures on radiographs.European radiology · 2026Article
- Alignment of Policy, Practice, and Patient Safety for Trustworthy AI in Radiology.Radiology. Artificial intelligence · 2026Article
- The bottleneck was never data or algorithms: building a learning utility for AI-enabled learning health systems.npj health systems · 2026Article
- Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology.Journal of imaging informatics in medicine · 2026Review
- DeepFAN, a transformer-based model for human-artificial intelligence collaborative assessment of incidental pulmonary nodules in CT scans: a multireader, multicase trial.Nature cancer · 2026Article
- An explainable imaging-clinical biomarker for non-small cell lung cancer prognostication based on normalised hotspot to centroid distance and [European journal of nuclear medicine and molecular imaging · 2026Article
- From Artificial Intelligence to Robotics: How to Navigate Technological Innovation in Radiology With the Gartner Hype Cycle.Academic radiology · 2026Review
- State of the AI: Post-Deployment Monitoring of Radiology-Focused Internally Developed AI.Mayo Clinic proceedings. Digital health · 2026Review
- Development and expert radiologist validation of a custom pipeline for simplification of oncology radiology reports using large language model.Frontiers in oncology · 2026Article
- Diagnostic Accuracy of GPT-4 With Vision in Neuroradiology Board-Style Exam Questions: Cross-Sectional Case-Based Study.JMIR neurotechnology · 2026Article
- Artificial Intelligence in Australian Dental and General Healthcare: A Scoping Review.Australian dental journal · 2025Article
- Patient Perspectives on Artificial Intelligence in Medical Imaging.Journal of participatory medicine · 2025Article
- Quantitative image quality metrics enable resource-efficient quality control of clinically applied AI-based reconstructions in MRI.Magma (New York, N.Y.) · 2025Article
- Inconsistency of AI in intracranial aneurysm detection with varying dose and image reconstruction.Scientific reports · 2025Article
- The Limitations of Artificial Intelligence in Head and Neck Oncology.Advances in therapy · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 6 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.
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.