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Medical student knowledge and critical appraisal of machine learning: a multicentre international cross-sectional study.

Charlotte BlacketerRoger ParnisKyle B FrankeMorganne WagnerDavid WangYiran TanLuke Oakden-RaynerSteve GallagherSeth W PerryJulio LicinioIan SymondsJosephine ThomasPaul DugganStephen Bacchi
Published in: Internal medicine journal (2021)
To utilise effectively tools that employ machine learning (ML) in clinical practice medical students and doctors will require a degree of understanding of ML models. To evaluate current levels of understanding, a formative examination and survey was conducted across three centres in Australia, New Zealand and the United States. Of the 245 individuals who participated in the study (response rate = 45.4%), the majority had difficulty with identifying weaknesses in model performance analysis. Further studies examining educational interventions addressing such ML topics are warranted.
Keyphrases
  • machine learning
  • medical students
  • clinical practice
  • artificial intelligence
  • healthcare
  • cross sectional
  • big data
  • physical activity
  • clinical trial
  • deep learning
  • study protocol
  • randomized controlled trial