ArticleAJNR. American journal of neuroradiology2023
Critical Appraisal of Artificial Intelligence-Enabled Imaging Tools Using the Levels of Evidence System.
Article in AJNR. American journal of neuroradiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Artificial intelligence for pediatric fracture detection: impact on diagnostic revisions and patient recall rates in a tertiary emergency setting.BMC emergency medicine · 2026Article
- AI-assisted hemorrhage detection following endovascular stroke treatment: a retrospective diagnostic accuracy study.Neurological research and practice · 2026Article
- Outcomes of Radiologist Recommendations for Temporal Bone CT to Assess Superior Semicircular Canal Dehiscence on Temporal Bone MRI.AJNR. American journal of neuroradiology · 2025Observational
- Empowering Data Sharing in Neuroscience: A Deep Learning Deidentification Method for Pediatric Brain MRIs.AJNR. American journal of neuroradiology · 2025Article
- Checklist Approach to Developing and Implementing AI in Clinical Settings: Instrument Development Study.JMIRx med · 2025Article
- Controversies in the Application of AI in Radiology-Is There Medico-Legal Support? Aspects from Romanian Practice.Diagnostics (Basel, Switzerland) · 2025Review
- Hepatic encephalopathy post-TIPS: Current status and prospects in predictive assessment.Computational and structural biotechnology journal · 2024Review
- Enhancing mortality prediction in patients with spontaneous intracerebral hemorrhage: Radiomics and supervised machine learning on non-contrast computed tomography.European journal of radiology open · 2024Article
- Automated Detection of Steno-Occlusive Lesion on Time-of-Flight MR Angiography: An Observer Performance Study.AJNR. American journal of neuroradiology · 2024Article
- A Radiomic "Warning Sign" of Progression on Brain MRI in Individuals with MS.AJNR. American journal of neuroradiology · 2024Article
- Article
- Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions.Thyroid : official journal of the American Thyroid Association · 2023Review
- Artificial intelligence in medicine and research - the good, the bad, and the ugly.Saudi journal of anaesthesiaReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Clinical adoption of an artificial intelligence-enabled imaging tool requires critical appraisal of its life cycle from development to implementation by using a systematic, standardized, and objective approach that can verify both its technical and clinical efficacy. Toward this concerted effort, the ASFNR/ASNR Artificial Intelligence Workshop Technology Working Group is proposing a hierarchal evaluation system based on the quality, type, and amount of scientific evidence that the artificial intelligence-enabled tool can demonstrate for each component of its life cycle. The current proposal is modeled after the levels of evidence in medicine, with the uppermost level of the hierarchy showing the strongest evidence for potential impact on patient care and health care outcomes. The intended goal of establishing an evidence-based evaluation system is to encourage transparency, foster an understanding of the creation of artificial intelligence tools and the artificial intelligence decision-making process, and to report the relevant data on the efficacy of artificial intelligence tools that are developed. The proposed system is an essential step in working toward a more formalized, clinically validated, and regulated framework for the safe and effective deployment of artificial intelligence imaging applications that will be used in clinical practice.
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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.