<p class="first" id="d1216393e212">The advent of computer graphic processing units,
improvement in mathematical models
and availability of big data has allowed artificial intelligence (AI) using machine
learning (ML) and deep learning (DL) techniques to achieve robust performance for
broad applications in social-media, the internet of things, the automotive industry
and healthcare. DL systems in particular provide improved capability in image, speech
and motion recognition as well as in natural language processing. In medicine, significant
progress of AI and DL systems has been demonstrated in image-centric specialties such
as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered
prospective clinical trials, have shown DL systems are accurate and effective in detecting
diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy
of prematurity, refractive error and in identifying cardiovascular risk factors and
diseases, from digital fundus photographs. There is also increasing attention on the
use of AI and DL systems in identifying disease features, progression and treatment
response for retinal diseases such as neovascular AMD and diabetic macular edema using
optical coherence tomography (OCT). Additionally, the application of ML to visual
fields may be useful in detecting glaucoma progression. There are limited studies
that incorporate clinical data including electronic health records, in AL and DL algorithms,
and no prospective studies to demonstrate that AI and DL algorithms can predict the
development of clinical eye disease. This article describes global eye disease burden,
unmet needs and common conditions of public health importance for which AI and DL
systems may be applicable. Technical and clinical aspects to build a DL system to
address those needs, and the potential challenges for clinical adoption are discussed.
AI, ML and DL will likely play a crucial role in clinical ophthalmology practice,
with implications for screening, diagnosis and follow up of the major causes of vision
impairment in the setting of ageing populations globally.
</p>