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Machine learning and artificial intelligence in research and healthcare✰,✰✰

  • Luc Rubinger
    Correspondence
    Corresponding author at: Center for Evidence-Based Orthopaedics, Division of Orthopaedics, Department of Surgery, McMaster University 293 Wellington Street North, Suite 110 Hamilton, ON, Canada L8L 8E7.
    Affiliations
    Division of Orthopaedics, Department of Surgery, McMaster University, Hamilton, ON Canada

    Centre for Evidence-Based Orthopaedics, 293 Wellington St. N, Suite 110, Hamilton, ON L8L 8E7 Canada
    Search for articles by this author
  • Aaron Gazendam
    Affiliations
    Division of Orthopaedics, Department of Surgery, McMaster University, Hamilton, ON Canada

    Centre for Evidence-Based Orthopaedics, 293 Wellington St. N, Suite 110, Hamilton, ON L8L 8E7 Canada
    Search for articles by this author
  • Seper Ekhtiari
    Affiliations
    Division of Orthopaedics, Department of Surgery, McMaster University, Hamilton, ON Canada

    Centre for Evidence-Based Orthopaedics, 293 Wellington St. N, Suite 110, Hamilton, ON L8L 8E7 Canada
    Search for articles by this author
  • Mohit Bhandari
    Affiliations
    Division of Orthopaedics, Department of Surgery, McMaster University, Hamilton, ON Canada

    Centre for Evidence-Based Orthopaedics, 293 Wellington St. N, Suite 110, Hamilton, ON L8L 8E7 Canada
    Search for articles by this author
Published:January 31, 2022DOI:https://doi.org/10.1016/j.injury.2022.01.046

      Highlights

      • Highlight 1: AI and ML are becoming cornerstones in the healthcare-research and are integral in our continued capitalization of robust patient EMR data.
      • Highlight 2: Considerations for the use and application of ML in healthcare settings include transparency, interpretability, and ethics.
      • Highlight 3: The future of AI and ML in healthcare research is exciting and expansive.

      Abstract

      Artificial intelligence (AI) is a broad term referring to the application of computational algorithms that can analyze large data sets to classify, predict, or gain useful conclusions. Under the umbrella of AI is machine learning (ML). ML is the process of building or learning statistical models using previously observed real world data to predict outcomes, or categorize observations based on ‘training’ provided by humans. These predictions are then applied to future data, all the while folding in the new data into its perpetually improving and calibrated statistical model. The future of AI and ML in healthcare research is exciting and expansive. AI and ML are becoming cornerstones in the medical and healthcare-research domains and are integral in our continued processing and capitalization of robust patient EMR data. Considerations for the use and application of ML in healthcare settings include assessing the quality of data inputs and decision-making that serve as the foundations of the ML model, ensuring the end-product is interpretable, transparent, and ethical concerns are considered throughout the development process. The current and future applications of ML include improving the quality and quantity of data collected from EMRs to improve registry data, utilizing these robust datasets to improve and standardized research protocols and outcomes, clinical decision-making applications, natural language processing and improving the fundamentals of value-based care, to name only a few.

      Keywords

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