ReviewEmergency radiology2023
AI tools in Emergency Radiology reading room: a new era of Radiology.
Review in Emergency radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Artificial intelligence in the emergency department-- applications, perceptions and limitations: a narrative review.World journal of emergency medicine · 2026Review
- Towards Automated Spine Fracture Detection on Whole-Body CT of Polytraumatized Patients.Journal of imaging · 2026Article
- Enhancing pediatric distal radius fracture detection: optimizing YOLOv8 with advanced AI and machine learning techniques.BMC medical imaging · 2025Article
- Development and optimization of AI algorithms for wrist fracture detection in children using a freely available dataset.Frontiers in pediatrics · 2023Article
- Impact of Artificial Intelligence Assistance on Diagnosing Traumatic Pneumothorax: A Comparison of Specialist and Non-Specialist Emergency Physicians.Acute medicine & surgeryArticle
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
Artificial intelligence tools in radiology practices have surged, with modules developed to target specific findings becoming increasingly prevalent and proving valuable in the daily emergency room radiology practice. The number of US Food and Drug Administration-cleared radiology-related algorithms has soared from just 10 in early 2017 to over 200 presently. This review will concentrate on the present utilization of AI tools in clinical ER radiology setting, including a brief discussion of the limitations of the technique. As radiologists, it is essential that we embrace this technology, comprehend its constraints, and use it to improve patient care.
Indexed as
Identifiers
37420044What 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.