ReviewEmergency radiology2024
Artificial intelligence in the detection of non-biological materials.
Review in Emergency radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Residual Oil-Based Contrast Medium Mimicking a Metallic Foreign Body After Cesarean Section: Diagnostic Value of AI-Assisted 3D CT Analysis and Prenatal MRI.The journal of obstetrics and gynaecology research · 2026Article
- Unintentionally retained surgical gauze presenting as chronic infected fistula to the surgical wound: a report of two cases.Patient safety in surgery · 2025Article
- Safety Culture and Human Factors in Foreign Object Management in Surgery.Healthcare (Basel, Switzerland) · 2025Article
- Modern endoscopist's toolbox: Innovations in foreign body removal.World journal of gastrointestinal endoscopy · 2025Review
- The diagnostic performance of automatic B-lines detection for evaluating pulmonary edema in the emergency department among novice point-of-care ultrasound practitioners.Emergency radiology · 2025Article
- Clinical implications of teleradiology in rheumatic and musculoskeletal diseases: improving rheumatic care.Rheumatology international · 2025Review
- Artificial intelligence model for automated surgical instrument detection and counting: an experimental proof-of-concept study.Patient safety in surgery · 2024Article
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
Artificial Intelligence (AI) has emerged as a transformative force within medical imaging, making significant strides within emergency radiology. Presently, there is a strong reliance on radiologists to accurately diagnose and characterize foreign bodies in a timely fashion, a task that can be readily augmented with AI tools. This article will first explore the most common clinical scenarios involving foreign bodies, such as retained surgical instruments, open and penetrating injuries, catheter and tube malposition, and foreign body ingestion and aspiration. By initially exploring the existing imaging techniques employed for diagnosing these conditions, the potential role of AI in detecting non-biological materials can be better elucidated. Yet, the heterogeneous nature of foreign bodies and limited data availability complicates the development of computer-aided detection models. Despite these challenges, integrating AI can potentially decrease radiologist workload, enhance diagnostic accuracy, and improve patient outcomes.
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.