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Artificial Intelligence in Orthopaedics: Clinical Performance, Limitations, and Translational Readiness-A Review.

Wojciech Michał Glinkowski1,2, Antonina Spalińska3, Agnieszka Wołk2

  • 1Center of Excellence "TeleOrto" for Telediagnostics and Treatment of Disorders and Injuries of the Locomotor System, Department of Medical Informatics and Telemedicine, Medical University of Warsaw, 02-091 Warsaw, Poland.

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|March 14, 2026
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Summary

Artificial intelligence (AI) is enhancing musculoskeletal care, particularly in imaging and surgical planning, with expert-level diagnostic performance. Further research is needed for widespread, equitable implementation in orthopaedics.

Keywords:
arthroplastyartificial intelligenceclinical implementationorthopaedic imagingosteoarthritispatient-reported outcomespredictive analyticsrehabilitation intelligence

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Area of Science:

  • Orthopaedic Surgery
  • Medical Imaging
  • Artificial Intelligence
  • Digital Health

Background:

  • Musculoskeletal disorders significantly contribute to global disability and healthcare costs.
  • Artificial intelligence (AI) offers transformative potential for data-driven musculoskeletal care.
  • AI applications span diagnostics, surgical planning, risk prediction, rehabilitation, and digital health.

Purpose of the Study:

  • To synthesize current evidence on AI applications in orthopaedics and musculoskeletal care.
  • To identify translational gaps and priorities for safe, ethical, and equitable AI implementation.
  • To review AI in diagnostic imaging, surgical planning, predictive analytics, rehabilitation, and system-level management.

Main Methods:

  • A structured narrative review was conducted using major scientific databases (PubMed, Scopus, Web of Science) and semantic search tools.
  • Searches focused on peer-reviewed articles from January 2019 to December 2025, analyzing clinical relevance and translational implications.
  • Forty clinically relevant studies were selected for detailed synthesis across five thematic AI application areas.

Main Results:

  • AI demonstrates mature evidence in fracture detection (90% sensitivity, 92% specificity) and implant identification (97-99% accuracy) in arthroplasty preoperative planning.
  • AI-assisted planning improves accuracy, potentially reducing intraoperative corrections, surgery time, and blood loss.
  • Predictive models show promise for risk stratification, but external validation and multicenter studies are limited; rehabilitation and teleorthopaedics applications are emerging.

Conclusions:

  • AI is increasingly integrated into musculoskeletal care, providing patient-centered decision support.
  • Widespread AI adoption is hindered by limited multicenter validation, dataset bias, and regulatory immaturity.
  • Future efforts must focus on prospective impact studies, local model revalidation, and adapting to regulatory requirements for safe and equitable implementation.