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)

Al-Zaiti, S. S., Alghwiri, A. A., Hu, X., Clermont, G., Peace, A., Macfarlane, P. and Bond, R. (2022) 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). European Heart Journal: Digital Health, 3(2), pp. 125-140. (doi: 10.1093/ehjdh/ztac016) (PMID:36713011) (PMCID:PMC9708024)

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.

• Item Type: Articles
• Additional Information: A.P. and R.B. are supported by the European Union’s INTERREG VA programme, managed by the Special EU Programmes Body (SEUPB). Their work is associated with the project—‘Centre for Personalised Medicine—Clinical Decision Making and Patient Safety’.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Macfarlane, Professor Peter
• Authors: Al-Zaiti, S. S., Alghwiri, A. A., Hu, X., Clermont, G., Peace, A., Macfarlane, P., and Bond, R.
• College/School: College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Robertson Centre
• Journal Name: European Heart Journal: Digital Health
• Publisher: Oxford University Press
• ISSN: 2634-3916
• ISSN (Online): 2634-3916
• Published Online: 12 April 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in European Heart Journal: Digital Health 3(2): 125-140
• Publisher Policy: Reproduced under a Creative Commons License