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Review
. 2020 Jul;68(7):1339-1346.
doi: 10.4103/ijo.IJO_1754_19.

Insights into the growing popularity of artificial intelligence in ophthalmology

Sreetama Dutt  1 , Anand Sivaraman  1 , Florian Savoy  2 , Ramachandran Rajalakshmi  3
Affiliations
Review

Insights into the growing popularity of artificial intelligence in ophthalmology

Sreetama Dutt et al. Indian J Ophthalmol. 2020 Jul.

Abstract

Artificial intelligence (AI) in healthcare is the use of computer-algorithms in analyzing complex medical data to detect associations and provide diagnostic support outputs. AI and deep learning (DL) find obvious applications in fields like ophthalmology wherein huge amount of image-based data need to be analyzed; however, the outcomes related to image recognition are reasonably well-defined. AI and DL have found important roles in ophthalmology in early screening and detection of conditions such as diabetic retinopathy (DR), age-related macular degeneration (ARMD), retinopathy of prematurity (ROP), glaucoma, and other ocular disorders, being successful inroads as far as early screening and diagnosis are concerned and appear promising with advantages of high-screening accuracy, consistency, and scalability. AI algorithms need equally skilled manpower, trained optometrists/ophthalmologists (annotators) to provide accurate ground truth for training the images. The basis of diagnoses made by AI algorithms is mechanical, and some amount of human intervention is necessary for further interpretations. This review was conducted after tracing the history of AI in ophthalmology across multiple research databases and aims to summarise the journey of AI in ophthalmology so far, making a close observation of most of the crucial studies conducted. This article further aims to highlight the potential impact of AI in ophthalmology, the pitfalls, and how to optimally use it to the maximum benefits of the ophthalmologists, the healthcare systems and the patients, alike.

Keywords: Age-related macular degeneration; anterior-segment diseases; artificial intelligence; cataract; deep learning; diabetic retinopathy; glaucoma; machine learning; ophthalmology; retinopathy of prematurity.

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Conflict of interest statement

None

Figures

Figure 1
Figure 1
(a) Interface for the inbuilt, automated, offline AI-algorithm, Medios-AI integrated into fundus on phone (FOP) to provide instant DR diagnosis. (b) Sample report generated showing heat maps highlighting DR lesions
See this image and copyright information in PMC

Comment in

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