The real-world application of artificial intelligence (AI),
machine learning (ML) and deep learning (DL), have
generated significant interest throughout the computer
science and medical communities in recent years. This
interest has been accompanied by no small amount of
hype. Though the term ‘ML’ was coined 50 years ago by
Arthur Samuel, who stated that machines should have the
ability to learn without being programmed,1 the advent
of the graphics processing unit (GPU) has enabled much
improved processing power and enabled new possibilities
with AI. DL—an approach that utilises multiple neural
networks to learn representation of data using multiple
levels of abstraction2—has revolutionised the computer
vision field, and achieved substantial jumps in diagnostic
performance for image recognition, speech recognition, and
natural language processing.2 In the technical world, DL
has been heavily used in autonomous vehicles,3 gaming4,5
and numerous smart phone applications. The availability
of different software (e.g. Caffe, Tensorflow), and the offthe-
shelf convolutional neural networks (e.g. AlexNet,
VGGNet, ResNet and GoogleNet) have removed barriers
to entry for many academics and clinicians, resulting in
the recent surge of interest within the medical settings.
To date, this technique has shown promising diagnostic
performance, across specialties including ophthalmology
(e.g. detection of diabetic retinopathy [DR], glaucoma and
age-related macular degeneration from fundus photographs
and optical coherence tomographs),6-11 radiology (e.g.
detection of tuberculosis from chest X-rays [CXRs],
intracranial haemorrhage from computed tomography of
the brain),12-15 and dermatology (e.g. detection of malignant
melanoma from skin photographs).
This article is available only as a PDF. Please click on “Download PDF” on top to view the full article.