ReviewCureus2025
Artificial Intelligence in Bone Fracture Detection: A Review of Evidence, Limitations, and Clinical Integration.
Review in Cureus, 2025. 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 and emergency physicians in the USA: enhancing clinical excellence while preserving physicians' judgment.World journal of emergency medicine · 2026Article
- Temporal Reproducibility of Fracture Interpretation in Forensic Radiography: A Multispecialty Comparison of Physicians and Vision Language Models Including Fracture Subtype Description.Tomography (Ann Arbor, Mich.) · 2026Article
- Can ChatGPT pass the polish national medical specialization examination in orthopedics and traumatology?Archives of orthopaedic and trauma surgery · 2026Article
- AI-Assisted Fracture Detection in Orthopedic and Trauma Imaging: Where It Works, Where It Fails, and Principles for Safe Clinical Deployment.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Orthopaedics: Clinical Performance, Limitations, and Translational Readiness-A Review.Journal of clinical medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical imaging is rapidly being improved by artificial intelligence (AI), with deep-learning systems performing well in radiography, CT, and MRI for fracture detection, classification, and localization. This narrative review examined recent evidence spanning different types of bone fractures, alongside soft-tissue injuries relevant to orthopedic decision-making. Across multiple meta-analyses and external validations, reported sensitivities and specificities commonly range from 0.85 to 0.95, while AI also supports workflow triage and reader confidence. Persistent gaps include limited generalizability, inconsistent reference standards, spectrum bias, regulatory and ethical challenges, and implementation costs. We outline pragmatic quality considerations and emphasize prospective, multi-center trials and transparent reporting for safe clinical integration. AI should augment clinicians, improving speed, accuracy, and overall 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.