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Radiographers' accuracy in interpreting acute musculoskeletal X-rays when supported by artificial intelligence - A
E Kjelle1, Y L V Larsson2, R Sivanandan3
1Department of Optometry, Radiography and Lighting Design, University of South-Eastern Norway, P.O. Box 4, 3199, Borre, Norway.
Radiography (London, England : 1995)
|July 14, 2026
Summary
AI-supported radiographers showed improved specificity in detecting skeletal injuries on X-rays, though sensitivity remained similar to AI alone. Performance varied, indicating room for optimization in AI-assisted diagnostic workflows.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence in Healthcare
Background:
- Artificial intelligence (AI) tools like BoneView™ are integrated into clinical practice for skeletal X-ray interpretation.
- This integration necessitates a new workflow where radiographers evaluate and act on AI-generated output.
- The study aimed to assess the diagnostic accuracy of radiographers when supported by AI in identifying skeletal injuries on adult trauma X-rays.
Purpose of the Study:
- To evaluate the accuracy of AI-supported radiographers in detecting skeletal injuries on adult trauma X-rays.
- To compare the diagnostic performance of AI alone versus AI-supported radiographers.
- To analyze the impact of radiographer experience on diagnostic accuracy.
Main Methods:
- A cross-sectional study involving 10 AI-supported diagnostic radiographers from 4 hospitals.
- Retrospective assessment of 542 acute musculoskeletal X-ray examinations using BoneView™ AI output.
- Calculation of sensitivity and specificity for AI and AI-supported radiographers, with radiologist reports as the reference standard.
Main Results:
- AI-supported radiographers achieved 94% sensitivity and 86% specificity, compared to AI alone (96% sensitivity, 71% specificity).
- Improved specificity was noted in pelvic/hip and foot/toe regions, but substantial inter-radiographer variability was observed.
- Radiographers with more experience (≥5 years) showed higher sensitivity but lower specificity.
Conclusions:
- Radiographer involvement improved specificity compared to AI alone, with minimal impact on sensitivity.
- Significant variability in performance and suboptimal specificity suggest potential for enhancement through training and AI refinement.
- Understanding AI-assisted diagnostic accuracy is crucial for optimizing triage, reducing delays, and managing workload in skeletal imaging.
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