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General medicine
Evidence-based medicine and machine learning: a partnership with a common purpose
  1. Ian Scott1,2,
  2. David Cook3,
  3. Enrico Coiera4
  1. 1Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia
  2. 2School of Clinical Medicine, The University of Queensland, Woolloongabba, Queensland, Australia
  3. 3Intensive Care, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia
  4. 4Australian Institute of Health Innovation, Macquarie University, Sydney, New South Wales, Australia
  1. Correspondence to Professor Ian Scott, Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Woolloongabba, QLD 4102, Australia; ian.scott{at}health.qld.gov.au

Abstract

From its origins in epidemiology, evidence-based medicine has promulgated a rigorous approach to assessing the validity, impact and applicability of hypothesis-driven empirical research used to evaluate the utility of diagnostic tests, prognostic tools and therapeutic interventions. Machine learning, a subset of artificial intelligence, uses computer programs to discover patterns and associations within huge datasets which are then incorporated into algorithms used to assist diagnoses and predict future outcomes, including response to therapies. How do these two fields relate to one another? What are their similarities and differences, their strengths and weaknesses? Can each learn from, and complement, the other in rendering clinical decision-making more informed and effective?

  • health informatics
  • information technology
  • general medicine
  • statistics & research methods
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Footnotes

  • Contributors IS conceived the idea, undertook the original research and drafted the manuscript. DC critically reviewed the manuscript, submitted additional concepts and assisted in writing the final version. EC critically reviewed the manuscript, submitted additional concepts and references, and assisted in writing the final version.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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