Machine learning and artificial intelligence in haematology

Roni Shouval, Joshua A. Fein, Bipin Savani, Mohamad Mohty, Arnon Nagler

Research output: Contribution to journalReview articlepeer-review

58 Scopus citations

Abstract

Digitalization of the medical record and integration of genomic methods into clinical practice have resulted in an unprecedented wealth of data. Machine learning is a subdomain of artificial intelligence that attempts to computationally extract meaningful insights from complex data structures. Applications of machine learning in haematological scenarios are steadily increasing. However, basic concepts are often unfamiliar to clinicians and investigators. The purpose of this review is to provide readers with tools to interpret and critically appraise machine learning literature. We begin with the elucidation of standard terminology and then review examples in haematology. Guidelines for designing and evaluating machine-learning studies are provided. Finally, we discuss limitations of the machine-learning approach.

Original languageEnglish
Pages (from-to)239-250
Number of pages12
JournalBritish Journal of Haematology
Volume192
Issue number2
DOIs
StatePublished - Jan 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 British Society for Haematology and John Wiley & Sons Ltd

Keywords

  • artificial intelligence
  • haematology
  • leukaemia
  • machine learning
  • prediction models

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