ArticleRadiology. Artificial intelligence2019
Fostering a Healthy AI Ecosystem for Radiology: Conclusions of the 2018 RSNA Summit on AI in Radiology.
Article in Radiology. Artificial intelligence, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Improving annotation efficiency for fully labeling a breast mass segmentation dataset.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Analyzing Barriers and Enablers for the Acceptance of Artificial Intelligence Innovations into Radiology Practice: A Scoping Review.Tomography (Ann Arbor, Mich.) · 2023Article
- A vendor-agnostic, PACS integrated, and DICOMcompatible software-server pipeline for testing segmentation algorithms within the clinical radiology workflow.Research square · 2023Article
- A vendor-agnostic, PACS integrated, and DICOM-compatible software-server pipeline for testing segmentation algorithms within the clinical radiology workflow.Frontiers in medicine · 2023Article
- Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer's disease?Frontiers in aging neuroscience · 2023Review
- An efficient magnetic resonance image data quality screening dashboard.Journal of applied clinical medical physics · 2022Article
- A Radiology-focused Review of Predictive Uncertainty for AI Interpretability in Computer-assisted Segmentation.Radiology. Artificial intelligence · 2021Review
- An Open-Source, Vender Agnostic Hardware and Software Pipeline for Integration of Artificial Intelligence in Radiology Workflow.Journal of digital imaging · 2020Article
- Will Artificial Intelligence Replace Radiologists?Radiology. Artificial intelligence · 2019Article
- The Rebirth of CAD: How Is Modern AI Different from the CAD We Know?Radiology. Artificial intelligence · 2019Article
- Impact of artificial intelligence on the choice of radiology as a specialty by medical students from the city of São Paulo.Radiologia brasileiraArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The 2018 RSNA Summit on AI in Radiology brought together a diverse group of stakeholders to identify and prioritize areas of need related to artificial intelligence in radiology. This article presents the proceedings of the summit with emphasis on RSNA's role in leading, organizing, and catalyzing change during this important time in radiology. © RSNA, 2019.
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.