A clinician's guide to artificial intelligence: how to critically appraise machine learning studies.
Faes, Livia Liu, Xiaoxuan Wagner, Siegfried K Fu, Dun Jack Balaskas, Konstantinos Sim, Dawn A Bachmann, Lucas M Keane, Pearse A Denniston, Alastair K
Faes, Livia
Liu, Xiaoxuan
Wagner, Siegfried K
Fu, Dun Jack
Balaskas, Konstantinos
Sim, Dawn A
Bachmann, Lucas M
Keane, Pearse A
Denniston, Alastair K
Abstract
In recent years, there has been considerable interest in the prospect of machine learning models demonstrating expert-level diagnosis in multiple disease contexts. However, there is concern that the excitement around this field may be associated with inadequate scrutiny of methodology and insufficient adoption of scientific good practice in the studies involving artificial intelligence in health care. This article aims to empower clinicians and researchers to critically appraise studies of clinical applications of machine learning, through: (1) introducing basic machine learning concepts and nomenclature; (2) outlining key applicable principles of evidence-based medicine; and (3) highlighting some of the potential pitfalls in the design and reporting of these studies.
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Date
2020-02-12
Type
Corrigendum
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Citation
Faes L, Liu X, Wagner SK, Fu DJ, Balaskas K, Sim DA, Bachmann LM, Keane PA, Denniston AK. A Clinician's Guide to Artificial Intelligence: How to Critically Appraise Machine Learning Studies. Transl Vis Sci Technol. 2020 Feb 12;9(2):7. doi: 10.1167/tvst.9.2.7. Erratum in: Transl Vis Sci Technol. 2020 Aug 21;9(9):33. doi: 10.1167/tvst.9.9.33