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Artificial intelligence in orthopedics: current applications, challenges, and future directions.

Sang Yoon Kim1, Byung Sun Choi1, Hyuk-Soo Han1,2

  • 1Department of Orthopaedics, Seoul National University Hospital, Seoul, Korea, Republic of.

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Artificial intelligence (AI) in orthopedics shows promise for risk prediction and imaging but faces implementation challenges. Future success hinges on rigorous validation and integration into clinical workflows for demonstrated net benefit.

Keywords:
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Area of Science:

  • Orthopedic research
  • Medical artificial intelligence
  • Clinical informatics

Background:

  • Artificial intelligence (AI) research in orthopedics is advancing rapidly.
  • A significant gap exists between AI technical development and its clinical application in orthopedics.
  • This review examines current AI applications and implementation barriers in orthopedic practice.

Purpose of the Study:

  • To summarize current AI applications in orthopedic practice.
  • To highlight barriers hindering the clinical implementation of AI in orthopedics.
  • To propose future directions for AI in orthopedic research and practice.

Main Methods:

  • Narrative review of current literature on AI in orthopedics.
  • Analysis of AI applications in perioperative risk prediction (machine learning), musculoskeletal imaging (deep learning), and workflow/decision support (large language models).
  • Identification of barriers including algorithmic opacity, performance degradation, and workflow integration issues.

Main Results:

  • AI applications like automated fracture detection and osteoarthritis grading are nearing clinical maturity.
  • Key barriers to routine adoption include lack of transparency, performance variability, and poor workflow fit.
  • Rigorous external validation and probability calibration are crucial for trustworthy AI.

Conclusions:

  • The clinical impact of AI in orthopedics depends on life-cycle governance and proven net benefit.
  • Prioritizing reliability and implementation science over retrospective performance benchmarks is essential.
  • Future AI development may involve multimodal "digital twin" approaches for patient-specific care.