ReviewCureus2024
A Review on the Use of Artificial Intelligence in Fracture Detection.
Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in the diagnostic imaging of developmental dysplasia of the hip: a systematic review.EFORT open reviews · 2026Pooled it
- Multivendor comparison study of artificial intelligence software for automated fracture detection in paediatric patients.Pediatric radiology · 2026Article
- The AI implementation gap in trauma radiography: standalone versus discretionary AI-integrated fracture detection.European radiology experimental · 2026Article
- Hip joint image quality screening based on the Diffusion Mamba model.Scientific data · 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
- Review
- Artificial Intelligence in Orthopaedics: Clinical Performance, Limitations, and Translational Readiness-A Review.Journal of clinical medicine · 2026Review
- FDA-Cleared Artificial Intelligence Medical Devices in Orthopaedic Surgery.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026Article
- Performance of ChatGPT-5 in Diagnosing Fractures on Proximal Humerus and Intertrochanteric Femur X-Rays.Journal of orthopaedic case reports · 2026Article
- Advances in Artificial Intelligence for Wrist Joint Injury Diagnosis.International journal of medical sciences · 2026Review
- Optimizing the power of AI for fracture detection: from blind spots to breakthroughs.Skeletal radiology · 2025Review
- AI in Fracture Detection: A Cross-Disciplinary Analysis of Physician Acceptance Using the UTAUT Model.Diagnostics (Basel, Switzerland) · 2025Article
- Detecting Fifth Metatarsal Fractures on Radiographs Through the Lens of Smartphones: The FIXUS AI Algorithm.Cureus · 2025Article
- Advancements in Machine Learning for Precision Diagnostics and Surgical Interventions in Interconnected Musculoskeletal and Visual Systems.Journal of clinical medicine · 2025Review
- Evolution of Artificial Intelligence in Medical Education From 2000 to 2024: Bibliometric Analysis.Interactive journal of medical research · 2025Article
- Artificial Intelligence Detection of Cervical Spine Fractures Using Convolutional Neural Network Models.Neurospine · 2024Article
- Artificial Intelligence-Based Applications for Bone Fracture Detection Using Medical Images: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Diagnosis and detection of bone fracture in radiographic images using deep learning approaches.Frontiers in medicine · 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) simulates intelligent behavior using computers with minimum human intervention. Recent advances in AI, especially deep learning, have made significant progress in perceptual operations, enabling computers to convey and comprehend complicated input more accurately. Worldwide, fractures affect people of all ages and in all regions of the planet. One of the most prevalent causes of inaccurate diagnosis and medical lawsuits is overlooked fractures on radiographs taken in the emergency room, which can range from 2% to 9%. The workforce will soon be under a great deal of strain due to the growing demand for fracture detection on multiple imaging modalities. A dearth of radiologists worsens this rise in demand as a result of a delay in hiring and a significant percentage of radiologists close to retirement. Additionally, the process of interpreting diagnostic images can sometimes be challenging and tedious. Integrating orthopedic radio-diagnosis with AI presents a promising solution to these problems. There has recently been a noticeable rise in the application of deep learning techniques, namely convolutional neural networks (CNNs), in medical imaging. In the field of orthopedic trauma, CNNs are being documented to operate at the proficiency of expert orthopedic surgeons and radiologists in the identification and categorization of fractures. CNNs can analyze vast amounts of data at a rate that surpasses that of human observations. In this review, we discuss the use of deep learning methods in fracture detection and classification, the integration of AI with various imaging modalities, and the benefits and disadvantages of integrating AI with radio-diagnostics.
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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.