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.
Original language | English |
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Article number | 7 |
Pages (from-to) | 7 |
Journal | Translational Vision Science & Technology |
Volume | 9 |
Issue number | 2 |
DOIs | |
Publication status | Published - 12 Feb 2020 |
Bibliographical note
Copyright 2020 The Authors.Keywords
- Artificial intelligence
- Critical appraisal
- Machine learning
ASJC Scopus subject areas
- Biomedical Engineering
- Ophthalmology