Artificial intelligence in orthopaedics: false hope or not? A narrative review along the line of Gartner's hype cycle

Machine Learning Consortium, Jacobien H. F. Oosterhoff, Job N. Doornberg*

*Corresponding author for this work

    Research output: Contribution to journalReview articlepeer-review

    3 Citations (Scopus)
    4 Downloads (Pure)

    Abstract

    Artificial Intelligence (AI) in general, and Machine Learn-ing (ML)-based applications in particular, have the potential to change the scope of healthcare, including orthopaedic surgery.

    The greatest benefit of ML is in its ability to learn from real world clinical use and experience, and thereby its capability to improve its own performance.

    Many successful applications are known in orthopaedics, but have yet to be adopted and evaluated for accuracy and efficacy in patients' care and doctors' workflows.

    The recent hype around AI triggered hope for development of better risk stratification tools to personalize orthopaedics in all subsequent steps of care, from diagnosis to treatment.

    Computer vision applications for fracture recognition show promising results to support decision-making, overcome bias, process high-volume workloads without fatigue, and hold the promise of even outperforming doctors in certain tasks.

    In the near future, AI-derived applications are very likely to assist orthopaedic surgeons rather than replace us. 'If the computer takes over the simple stuff, doctors will have more time again to practice the art of medicine'.(76)

    Original languageEnglish
    Pages (from-to)593-603
    Number of pages11
    JournalEfort open reviews
    Volume5
    Issue number10
    DOIs
    Publication statusPublished - Oct-2020

    Keywords

    • artificial intelligence
    • computer vision
    • data-driven medicine
    • machine learning
    • orthopaedic surgery
    • orthopaedic trauma
    • personalized medicine
    • prediction tools
    • DISTAL RADIAL FRACTURES
    • EMERGENCY-DEPARTMENTS
    • TRAUMA RADIOGRAPHS
    • USERS GUIDES
    • PREDICTION
    • MODELS
    • CALIBRATION
    • IMPACT

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