Artificial intelligence in ophthalmology: Current applications and emerging issues

Rachael C. Heath Jeffery, Marcus Smith

Research output: Contribution to journalLetterpeer-review

1 Citation (Scopus)


In ophthalmology, artificial intelligence (AI) based on deep learning (DL) is already being applied to fundus photographs, optical coherence tomography (OCT) and visual fields (VFs).1 This has predominately focused on screening and grading in diabetic retinopathy, retinopathy of prematurity (ROP), glaucoma and age-related macular degeneration.1-3 AI can provide patients with greater access to screening, diagnosis and monitoring of major ocular diseases in primary or remote healthcare settings.2 It is most useful for identifying patterns in large data sets that meet certain diagnostic criteria, incorporating automation to perform laborious tasks and simplify complex procedures.2 Furthermore, new diagnostic and prognostic information may be gained by integrating data sets gathered from fundus photos and OCT images with laboratory tests and other types of medical imaging.
Original languageEnglish
Pages (from-to)536-537
Number of pages2
JournalClinical and Experimental Ophthalmology
Issue number4
Publication statusPublished - 1 May 2020
Externally publishedYes


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