Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Med Imaging. Oct 28, 2021; 2(5): 95-103
Published online Oct 28, 2021. doi: 10.35711/aimi.v2.i5.95
Artificial intelligence in ophthalmology and visual sciences: Current implications and future directions
Smaha Jahangir, Hashim Ali Khan
Smaha Jahangir, School of Optometry, The University of Faisalabad, Faisalabad, Punjab 38000, Pakistan
Hashim Ali Khan, Department of Ophthalmology, SEHHAT Foundation, Gilgit 15100, Gilgit-Baltistan, Pakistan
Author contributions: Jahangir S searched the literature and wrote the initial draft; Khan HA revised and edited the manuscript; both authors were involved in the basic idea and creating the outline of the review.
Conflict-of-interest statement: There is no conflict of interest among authors nor any source of bias towards this research.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Hashim Ali Khan, Department of Ophthalmology, SEHHAT Foundation, Main KKH, Danyore, Gilgit 15100, Gilgit-Baltistan, Pakistan.
Received: June 3, 2021
Peer-review started: June 3, 2021
First decision: June 23, 2021
Revised: June 30, 2021
Accepted: October 22, 2021
Article in press: October 27, 2021
Published online: October 28, 2021
Processing time: 145 Days and 12.9 Hours

Since its inception in 1959, artificial intelligence (AI) has evolved at an unprecedented rate and has revolutionized the world of medicine. Ophthalmology, being an image-driven field of medicine, is well-suited for the implementation of AI. Machine learning (ML) and deep learning (DL) models are being utilized for screening of vision threatening ocular conditions of the eye. These models have proven to be accurate and reliable for diagnosing anterior and posterior segment diseases, screening large populations, and even predicting the natural course of various ocular morbidities. With the increase in population and global burden of managing irreversible blindness, AI offers a unique solution when implemented in clinical practice. In this review, we discuss what are AI, ML, and DL, their uses, future direction for AI, and its limitations in ophthalmology.

Keywords: Artificial intelligence, Ophthalmology, Retina, Machine learning, Eye care

Core Tip: Machine learning and artificial intelligence have evolved rapidly in recent years. Powerful machines and futuristic algorithms are bringing many possibilities towards the utilization of artificial intelligence in medical sciences. Ophthalmology is versatile in its adapting to newer and novel technologies earlier than other fields. Machine learning techniques assist clinicians and researchers in the detection and diagnosis of diseases as well as quantification of different disease biomarkers from ocular images. Interestingly, recent innovations like auto-machine learning has made it possible for clinicians, with little knowledge in computing and mathematics, to partake in creating, modifying, and training models tailored to their area of interest.