Evidence-based medicine and machine learning: a partnership with a common purpose

Ian Scott, David Cook, Enrico Coiera

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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?
Original languageEnglish
Pages (from-to)290-294
Number of pages5
JournalBMJ Evidence-Based Medicine
Volume26
Issue number6
Early online date19 Aug 2020
DOIs
Publication statusPublished - 1 Dec 2021

Keywords

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

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