ArticleJournal of orthopaedic surgery and research2024
The potential benefit of artificial intelligence regarding clinical decision-making in the treatment of wrist trauma patients.
Article in Journal of orthopaedic surgery and research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- Advances in Artificial Intelligence for Wrist Joint Injury Diagnosis.International journal of medical sciences · 2026Review
- "Could She/He Walk Out of the Hospital?": Implementing AI Models for Recovery Prediction and Doctor-Patient Communication in Major Trauma.Diagnostics (Basel, Switzerland) · 2025Article
- Advanced feature fusion of radiomics and deep learning for accurate detection of wrist fractures on X-ray images.BMC musculoskeletal disorders · 2025Article
- A model to simulate human cardio-respiratory responses to different fluid resuscitation treatments after hemorrhagic injury.Frontiers in physiology · 2025Article
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Authors and funding
3 authors.
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Abstract
purposeThe implementation of artificial intelligence (AI) in health care is gaining popularity. Many publications describe powerful AI-enabled algorithms. Yet there's only scarce evidence for measurable value in terms of patient outcomes, clinical decision-making or socio-economic impact. Our aim was to investigate the significance of AI in the emergency treatment of wrist trauma patients.
methodTwo groups of physicians were confronted with twenty realistic cases of wrist trauma patients and had to find the correct diagnosis and provide a treatment recommendation. One group was assisted by an AI-enabled application which detects and localizes distal radius fractures (DRF) with near-to-perfect precision while the other group had no help. Primary outcome measurement was diagnostic accuracy. Secondary outcome measurements were required time, number of added CT scans and senior consultations, correctness of the treatment, subjective and objective stress levels.
resultsThe AI-supported group was able to make a diagnosis without support (no additional CT, no senior consultation) in significantly more of the cases than the control group (75% vs. 52%, p = 0.003). The AI-enhanced group detected DRF with superior sensitivity (1.00 vs. 0.96, p = 0.06) and specificity (0.99 vs. 0.93, p = 0.17), used significantly less additional CT scans to reach the correct diagnosis (14% vs. 28%, p = 0.02) and was subjectively significantly less stressed (p = 0.05).
conclusionThe results indicate that physicians can diagnose wrist trauma more accurately and faster when aided by an AI-tool that lessens the need for extra diagnostic procedures. The AI-tool also seems to lower physicians' stress levels while examining cases. We anticipate that these benefits will be amplified in larger studies as skepticism towards the new technology diminishes.
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