ArticleProceedings of the National Academy of Sciences of the United States of America2018
Deep neural network improves fracture detection by clinicians.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07673991 (Comparison of the Diagnostic Reliability of ChatGPT in Letournel-Judet Acetabulum Fracture Classification Based on Judet Radiographs According to Orthopaedic Residents), which is not on this map. Cited by 251 papers, 12 of them syntheses that pooled it.
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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.
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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.
Comparison of the Diagnostic Reliability of ChatGPT in Letournel-Judet Acetabulum Fracture Classification Based on Judet Radiographs According to Orthopaedic Residents
Who cites it
251 citing papers in PubMed, 12 syntheses or guidelines pooled it, 589 citations in OpenAlex.
- Enhanced fracture detection on radiographs with AI assistance for clinicians: a systematic review and meta-analysis.Annals of medicine · 2026Pooled it
- Diagnostic accuracy of AI-assisted versus independent physician interpretation for bone fractures: a systematic review and meta-analysis.European radiology experimental · 2026Pooled it
- Diagnostic accuracy of artificial intelligence compared to family physicians and dermatologists for skin conditions: a systematic review and meta-analysis.BMC primary care · 2025Pooled it
- Impact of deep learning on pediatric elbow fracture detection: a systematic review and meta-analysis.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2025Pooled it
- Diagnostic performance of neural network algorithms in skull fracture detection on CT scans: a systematic review and meta-analysis.Emergency radiology · 2025Pooled it
- AI for detection, classification and prediction of loss of alignment of distal radius fractures; a systematic review.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2024Pooled it
- Artificial intelligence in commercial fracture detection products: a systematic review and meta-analysis of diagnostic test accuracy.Scientific reports · 2024Pooled it
- Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis.Radiology · 2022Pooled it
- Augmenting Osteoporosis Imaging with Machine Learning.Current osteoporosis reports · 2021Pooled it
- Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit.Intensive care medicine · 2021Pooled it
- Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review.JAMA network open · 2021Pooled it
- Diagnostic accuracy & clinical importance of AI confidence for extremity fracture detection: 2,508-patient retrospective cohort.Emergency radiology · 2026Article
- Article
- Automated classification of musculoskeletal abnormalities using a dynamic ensemble of deep vision models.Medical & biological engineering & computing · 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
- Article
- Foundation models in radiology: a primer for pediatric radiologists.Pediatric radiology · 2026Review
- Deep learning for radiographic differentiation between lateral malleolar avulsion fractures and subfibular ossicles.iScience · 2026Article
- Interpretable deep learning for rotator cuff calcific tendinopathy diagnosis: a multi-center study.Scientific reports · 2026Article
191 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
11 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Suspected fractures are among the most common reasons for patients to visit emergency departments (EDs), and X-ray imaging is the primary diagnostic tool used by clinicians to assess patients for fractures. Missing a fracture in a radiograph often has severe consequences for patients, resulting in delayed treatment and poor recovery of function. Nevertheless, radiographs in emergency settings are often read out of necessity by emergency medicine clinicians who lack subspecialized expertise in orthopedics, and misdiagnosed fractures account for upward of four of every five reported diagnostic errors in certain EDs. In this work, we developed a deep neural network to detect and localize fractures in radiographs. We trained it to accurately emulate the expertise of 18 senior subspecialized orthopedic surgeons by having them annotate 135,409 radiographs. We then ran a controlled experiment with emergency medicine clinicians to evaluate their ability to detect fractures in wrist radiographs with and without the assistance of the deep learning model. The average clinician's sensitivity was 80.8% (95% CI, 76.7-84.1%) unaided and 91.5% (95% CI, 89.3-92.9%) aided, and specificity was 87.5% (95 CI, 85.3-89.5%) unaided and 93.9% (95% CI, 92.9-94.9%) aided. The average clinician experienced a relative reduction in misinterpretation rate of 47.0% (95% CI, 37.4-53.9%). The significant improvements in diagnostic accuracy that we observed in this study show that deep learning methods are a mechanism by which senior medical specialists can deliver their expertise to generalists on the front lines of medicine, thereby providing substantial improvements to patient care.
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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.