Machine learning in clinical practice: prospects and pitfalls

Ian A Scott, David Cook, Enrico W Coiera and Brent Richards
Med J Aust 2019; 211 (5): . || doi: 10.5694/mja2.50294
Published online: 2 September 2019

Machine learning has huge potential to enhance clinical decision making, but there are still many limitations

Machine learning (ML), a subdiscipline of artificial intelligence, encompasses a family of computerised (machine) methods that identify (learn) patterns in large (training) datasets not detectable to humans (Box 1). Identified patterns are then encoded in a computer model or algorithm which is then tested and validated on new data. Three basic ML types exist (Box 2), with supervised and reinforcement learning being used most frequently.

  • Ian A Scott1,2
  • David Cook1
  • Enrico W Coiera3
  • Brent Richards4

  • 1 Princess Alexandra Hospital, Brisbane, QLD
  • 2 University of Queensland, Brisbane, QLD
  • 3 Centre for Health Informatics, Macquarie University, Sydney, NSW
  • 4 Gold Coast Hospital and Health Service, Gold Coast, QLD


Competing interests:

Brent Richards has received non‐financial support from Amazon Web Services and non‐financial support from Microsoft.


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