Artificial intelligence in ophthalmology and visual sciences: Current implications and future directions

doi:10.35711/aimi.v2.i5.95

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Oct 28, 2021 (publication date) through Mar 5, 2026

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Artificial Intelligence in Medical Imaging

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2644-3260

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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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.

Corresponding author: Hashim Ali Khan, Department of Ophthalmology, SEHHAT Foundation, Main KKH, Danyore, Gilgit 15100, Gilgit-Baltistan, Pakistan. retinadr.hashimalikhan@gmail.com

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

Core Tip

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.