  Dry eye disease (DED) has a prevalence of between 5 and 50\%, depending on
the diagnostic criteria used and population under study. However, it remains
one of the most underdiagnosed and undertreated conditions in ophthalmology.
Many tests used in the diagnosis of DED rely on an experienced observer for
image interpretation, which may be considered subjective and result in
variation in diagnosis. Since artificial intelligence (AI) systems are capable
of advanced problem solving, use of such techniques could lead to more
objective diagnosis. Although the term `AI' is commonly used, recent success in
its applications to medicine is mainly due to advancements in the sub-field of
machine learning, which has been used to automatically classify images and
predict medical outcomes. Powerful machine learning techniques have been
harnessed to understand nuances in patient data and medical images, aiming for
consistent diagnosis and stratification of disease severity. This is the first
literature review on the use of AI in DED. We provide a brief introduction to
AI, report its current use in DED research and its potential for application in
the clinic. Our review found that AI has been employed in a wide range of DED
clinical tests and research applications, primarily for interpretation of
interferometry, slit-lamp and meibography images. While initial results are
promising, much work is still needed on model development, clinical testing and
standardisation.
