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 and Denniston, Alastair K (2020) A Clinician's Guide to Artificial Intelligence: How to Critically Appraise Machine Learning Studies. Translational vision science & technology, 9 (2). p. 7. ISSN 2164-2591.

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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.

Item Type: Article
Subjects: W Public health. Health statistics. Occupational health. Health education
WW Eyes. Ophthalmology
Divisions: Ambulatory Care > Ophthalmology
Related URLs:
Depositing User: Jamie Edgar
Date Deposited: 31 Jul 2020 14:07
Last Modified: 31 Jul 2020 14:07
URI: http://www.repository.uhblibrary.co.uk/id/eprint/3332

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