A Clinician's Guide to Artificial Intelligence: How to Critically Appraise Machine Learning Studies

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 insufficie...

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Bibliographic Details
Published inTranslational vision science & technology Vol. 9; no. 2; p. 7
Main Authors Faes, Livia, Liu, Xiaoxuan, Wagner, Siegfried K., Fu, Dun Jack, Balaskas, Konstantinos, Sim, Dawn A., Bachmann, Lucas M., Keane, Pearse A., Denniston, Alastair K.
Format Journal Article
LanguageEnglish
Published United States The Association for Research in Vision and Ophthalmology 12.02.2020
Subjects
Artificial Intelligence
Delivery of Health Care
Machine Learning
Special Issue
critical appraisal
machine learning
artificial intelligence
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Summary: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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ISSN:2164-2591
2164-2591
DOI:10.1167/tvst.9.2.7