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Updated: Dec 13, 2025
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A Novel Application of Musculoskeletal Ultrasound Imaging
Published on: September 17, 2013
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Does Artificial Intelligence Outperform Natural Intelligence in Interpreting Musculoskeletal Radiological Studies? A
Olivier Q Groot1, Michiel E R Bongers1, Paul T Ogink2
1O. Q. Groot, M. E. R. Bongers, A. V. Karhade, J. H. Schwab, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Clinical Orthopaedics and Related Research
|August 3, 2020
Summary
Machine learning (ML) models show comparable performance to clinicians in musculoskeletal imaging. ML can enhance clinician performance as a supplement, not a replacement, for clinical intelligence.
Area of Science:
- Artificial Intelligence in Medicine
- Machine Learning Applications
- Orthopaedic Imaging Diagnostics
Background:
- Machine learning (ML) is advancing artificial intelligence, with existing orthopaedic applications in diagnostics and post-surgical prediction.
- A systematic review is needed to compare ML models and clinicians in musculoskeletal imaging interpretation.
- This review assesses ML's current role in aiding orthopaedists with musculoskeletal image analysis.
Purpose of the Study:
- To compare the diagnostic performance (accuracy, sensitivity, specificity) of ML models versus clinicians in musculoskeletal imaging.
- To analyze performance based on imaging modality (radiographs, MRI, ultrasound) and clinician specialty.
- To evaluate the impact of ML-aided clinician performance compared to unaided clinicians.
Main Methods:
- Systematic review of PubMed, Embase, and Cochrane Library up to October 1, 2019.
- Included 12 studies comparing ML models head-to-head with clinicians in musculoskeletal image analysis.
- Quality assessment using MINORS checklist; quantitative synthesis of performance metrics (accuracy, sensitivity, specificity).
Main Results:
- ML models showed minimal improvement over clinicians in accuracy (3%) and sensitivity (0.06%), and were equivalent in specificity.
- ML models performed better on plain radiographs than MRIs; orthopaedists and radiologists performed similarly to ML models.
- ML-aided clinicians demonstrated significant improvements, with a 47% decrease in misinterpretation rates and increased specificity.
Conclusions:
- Currently, ML models offer comparable performance to clinicians in musculoskeletal imaging assessment.
- ML models are best utilized as technical supplements to enhance, not replace, clinical judgment.
- Future research should focus on ML's complementary role, improving transparency, reducing bias, and assessing clinical feasibility.
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