Comparison of the Difference in Bone Age Readings Among Radiologists Using Artificial Intelligence
Trial Snapshot
- Phase
- Not Applicable
- Status
- Enrolling By Invitation
- Sponsor
- Cheng-Hsin General Hospital
- Enrollment
- 8
- Locations
- 1
- Primary Endpoint
- Difference of Skeletal Age Estimate
Study Overview
Brief Summary
AI-Assisted Bone Age Assessment
Detailed Description
Artificial intelligence (AI) has gained great advancement in the application in clinical practice. However, this might introduce the automation bias, that the clinician over-rely on the incorrect advise from AI. The automation bias could have a great impact on the clinical decisions in an AI era. However, efforts to mitigate the automation bias tend to focus on upgrading AI performance and reducing bias in algorithms, which neglect the role of users. The aim of this study is to investigates the impact of the automation bias on the bone age assessment among radiologists with different seniority.
Study Design
- Study Type
- Interventional
- Allocation
- Randomized
- Intervention Model
- Crossover
- Primary Purpose
- Diagnostic
- Masking
- Single (Participant)
Eligibility Criteria
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •Exams read by radiologists who interpret pediatric skeletal age exams and verbally consent to participate
Exclusion Criteria
- •Exams containing more than one radiograph will not be included. No further exclusion criteria will be applied on the basis of image quality metrics or manufacturers
Arms & Interventions
True AI
Intervention: True AI (Device)
Fake AI
Intervention: Fake AI (Device)
Outcomes
Primary Outcomes
Difference of Skeletal Age Estimate
Time Frame: Five months
Mean absolute difference of bone age assessment between true AI-assisted and fake AI-assisted among radiologists
Secondary Outcomes
No secondary outcomes reported