Artificial intelligence and prediction of cardiometabolic disease: Systematic review of model performance and potential benefits in indigenous populations.

Journal: Artificial intelligence in medicine
PMID: 37100506

Abstract

BACKGROUND: Indigenous peoples often have higher rates of morbidity and mortality associated with cardiometabolic disease (CMD) than non-Indigenous people and this may be even more so in urban areas. The use of electronic health records and expansion of computing power has led to mainstream use of artificial intelligence (AI) to predict the onset of disease in primary health care (PHC) settings. However, it is unknown if AI and in particular machine learning is used for risk prediction of CMD in Indigenous peoples.

Authors

  • Keunwoo Jeong
    Faculty of Medicine, University of Queensland, Brisbane, Australia. Electronic address: k.jeong@uq.net.au.
  • Alistair R Mallard
    Faculty of Medicine, University of Queensland, Brisbane, Australia; Poche Centre for Indigenous Health, University of Queensland, Brisbane, Australia.
  • Leanne Coombe
    Faculty of Medicine, University of Queensland, Brisbane, Australia; Poche Centre for Indigenous Health, University of Queensland, Brisbane, Australia; Faculty of Health and Behavioural Sciences, University of Queensland, Brisbane, Australia.
  • James Ward
    Faculty of Medicine, University of Queensland, Brisbane, Australia; Poche Centre for Indigenous Health, University of Queensland, Brisbane, Australia; School of Public Health, University of Queensland, Brisbane, Australia; Faculty of Health and Behavioural Sciences, University of Queensland, Brisbane, Australia.

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