A clinician’s guide to understanding and critically appraising machine learning studies: a checklist for Ruling Out Bias Using Standard Tools in Machine Learning (ROBUST-ML)

Salah Al-Zaiti, Alaa A. Alghwiri, Xiao Hu, Gilles Clermont, Aaron Peace, Peter Macfarlane, RR Bond

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
97 Downloads (Pure)

Abstract

Developing functional machine learning (ML)-based models to address unmet clinical needs requires unique considerations for optimal clinical utility. Recent debates about the rigours, transparency, explainability, and reproducibility of ML models, terms which are defined in this article, have raised concerns about their clinical utility and suitability for integration in current evidence-based practice paradigms. This featured article focuses on increasing the literacy of ML among clinicians by providing them with the knowledge and tools needed to understand and critically appraise clinical studies focused on ML. A checklist is provided for evaluating the rigour and reproducibility of the four ML building blocks: data curation, feature engineering, model development, and clinical deployment. Checklists like this are important for quality assurance and to ensure that ML studies are rigourously and confidently reviewed by clinicians and are guided by domain knowledge of the setting in which the findings will be applied. Bridging the gap between clinicians, healthcare scientists, and ML engineers can address many shortcomings and pitfalls of ML-based solutions and their potential deployment at the bedside.
Original languageEnglish
Pages (from-to)125-140
Number of pages17
JournalEuropean Heart Journal - Digital Health
Volume3
Issue number2
Early online date12 Apr 2022
DOIs
Publication statusPublished online - 12 Apr 2022

Keywords

  • machine learning
  • healthcare
  • critical appraisal
  • bias
  • quality
  • guidelines

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