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Artificial intelligence and machine learning | applications in musculoskeletal physiotherapy.

Christopher Tack1

  • 1Guy's and St Thomas' NHS Foundation Trust, Guy's Hospital, Great Maze Pond, SE1 9RT, London, UK.

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Artificial intelligence (AI) and machine learning (ML) can improve healthcare by automating decisions and predictions using patient data. These technologies offer significant potential for enhancing musculoskeletal medicine and physiotherapy practices.

Keywords:
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Area of Science:

  • Mathematical engineering
  • Artificial intelligence
  • Machine learning

Background:

  • Artificial intelligence (AI) offers potential to enhance healthcare delivery, decision-making, and patient engagement.
  • Machine learning (ML), a subset of AI, automates decision-making and predictions using patient data.

Purpose of the Study:

  • Review key applications of supervised and unsupervised ML in musculoskeletal medicine.
  • Examine literature to identify areas where ML performance equals or exceeds human accuracy.
  • Focus on diagnostic imaging, patient measurement data, and clinical decision support.

Main Methods:

  • Literature review of supervised and unsupervised machine learning applications.
  • Analysis of current research in musculoskeletal medicine.
  • Comparison of ML performance against human levels of accuracy.

Main Results:

  • ML demonstrates potential in diagnostic imaging, patient measurement, and clinical decision support within musculoskeletal medicine.
  • Identified areas where ML performance is comparable or superior to human expertise.
  • Potential for ML to enhance physiotherapy through data analysis, classification, and prediction.

Conclusions:

  • Intelligent machines can automate tasks in physiotherapy, improving data analysis, classification, and prediction.
  • Physiotherapists should increase awareness and experience with emerging ML technologies.
  • Data literacy is crucial for professional development, enabling ML application and IT system preparation.