A Clinician's Guide to Artificial Intelligence: How to Critically Appraise Machine Learning Studies
TL
TL;DR
Clinicians and researchers are empowered to critically appraise studies of clinical applications of machine learning, through introducing basic machine learning concepts and nomenclature and highlighting some of the potential pitfalls in the design and reporting of these studies.
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.
References48 items
1
Extension of the CONSORT and SPIRIT statements
2
A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis.
3
Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed
4
Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition.
5