AI in Fracture Detection: A Cross-Disciplinary Analysis of Physician Acceptance Using the UTAUT Model.
Martin Breitwieser, Stephan Zirknitzer, Karolina Poslusny...
https://pubmed.ncbi.nlm.nih.gov/40870969Actively Recruiting
Phase Not Applicable
All Genders
ID06754137
Evaluating the Cost-Efficiency and Workflow Impact of AI-Supported Fracture Detection in an Orthopedic Emergency Care Unit
Led by Salzburger Landeskliniken · Updated on 2026-01-22
4800
Participants Needed
3
Research Sites
N/A
Total Duration
On this page
Sponsors
S
Salzburger Landeskliniken
Lead Sponsor
K
Klinikum Nürnberg
Collaborating Sponsor
AI-Summary
What this Trial Is About
Researchers are evaluating whether artificial intelligence (AI) can help doctors detect broken bones, joint effusions, dislocations, and bone lesions more quickly and accurately in emergency room settings. This prospective, randomized, controlled trial compares traditional diagnostic methods to AI-assisted X-ray interpretation. The study also aims to see if AI can save time and reduce healthcare costs while improving clinical workflows in orthopedic emergency care. Participants will be assigned to one of two groups: one using standard physician interpretation of X-rays without AI assistance, and the other using AI tools (Aidoc or Gleamer BoneView) integrated into the hospital's imaging system to highlight potential injuries in real time. Both groups will receive standard X-ray imaging of injured arms or legs. The AI serves as a diagnostic aid, with physicians making final diagnoses. The study includes around 4,800 patients presenting with isolated extremity injuries or joint complaints. During the study, participants will undergo routine X-ray imaging and have their images reviewed with or without AI support depending on group assignment. Researchers will collect baseline demographic and clinical data and assess diagnostic accuracy, time to diagnosis, physician confidence, and cost efficiency. Diagnoses will be independently reviewed by experts to ensure accuracy. The study adheres to ethical guidelines and requires informed consent from participants. Results may inform future use of AI in emergency care settings.
CONDITIONS
Brief Title
Assessing AI-Supported Fracture Detection in Emergency Care Units
Who Can Participate
All Genders
Eligibility Criteria
You may qualify if you...
- Presenting to the emergency department with an isolated injury or joint complaint
- Patients able and willing to provide informed consent
You will not qualify if you...
- Injuries or complaints involving multiple body regions
- Prior imaging of the affected extremity or region within the past 6 months
- Contraindications to X-ray imaging, such as pregnancy or severe instability
- Participation in other ongoing studies that may interfere
- Inability to provide consent due to cognitive impairment or language barriers without a representative available
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
1
2
3
Your Study Journey
Screening
Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
1 visit (in-person)
Diagnostic Evaluation
Duration - Within 4 hours of emergency department presentation
Participants undergo standard X-ray imaging of the affected extremity or joint. For those in the intervention group, AI-assisted fracture detection aids physicians in interpreting the images, while the control group receives standard physician interpretation without AI assistance.
1 visit during emergency department visit
Long-term Monitoring
Duration - Approximately 6 months
Cost-efficiency of the diagnostic workflow is assessed across all enrolled participants approximately 6 months after study initiation.
No additional visits; participants are followed through routine care and data collection
Trial Site Locations
Total: 3 locations
1
Landesklinik Hallein, Salzburger Landeskliniken
Hallein, Austria, 5400
Not Yet Recruiting
2
University Hospital Salzburg, Salzburger Landeskliniken
Salzburg, Austria, 5020
Actively Recruiting
3
University Hosptial Nuremberg, Klinikum Nürnberg
Nuremberg, Germany, 90471
Actively Recruiting
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Research Team
M
Martin Breitwieser, MD, MBA, BSc
How is the study designed?
Study Type
INTERVENTIONAL
Masking
NONE
Allocation
RANDOMIZED
Model
PARALLEL
Primary Purpose
DIAGNOSTIC
Number of Arms
2
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