Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2021 May-Jun;10(3):299-306.
doi: 10.1097/APO.0000000000000400.

Considerations for Artificial Intelligence Real-World Implementation in Ophthalmology: Providers' and Patients' Perspectives

Affiliations
Free article
Review

Considerations for Artificial Intelligence Real-World Implementation in Ophthalmology: Providers' and Patients' Perspectives

Rachel Marjorie Wei Wen Tseng et al. Asia Pac J Ophthalmol (Phila). 2021 May-Jun.
Free article

Abstract

Artificial Intelligence (AI), in particular deep learning, has made waves in the health care industry, with several prominent examples shown in ophthalmology. Despite the burgeoning reports on the development of new AI algorithms for detection and management of various eye diseases, few have reached the stage of regulatory approval for real-world implementation. To better enable real-world translation of AI systems, it is important to understand the demands, needs, and concerns of both health care professionals and patients, as providers and recipients of clinical care are impacted by these solutions. This review outlines the advantages and concerns of incorporating AI in ophthalmology care delivery, from both the providers' and patients' perspectives, and the key enablers for seamless transition to real-world implementation.

PubMed Disclaimer

Conflict of interest statement

The authors have no funding or conflicts of interest to declare.

References

    1. Gunasekeran DV, Tham YC, Ting DSW, et al. Digital health during COVID-19: lessons from operationalising new models of care in ophthalmology. Lancet Digit Health 2021; 3:e124–e134.
    1. Wong MYZ, Gunasekeran DV, Nusinovici S, et al. Telehealth demand trends during the COVID-19 pandemic in the top 50 most affected countries: infodemiological evaluation. JMIR Public Health Surveill 2021; 7:e24445.
    1. Gunasekeran DV, Tseng R, Tham YC, et al. Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies. NPJ Digit Med 2021; 4:40.
    1. Gunasekeran DV, Wong TY. Artificial intelligence in ophthalmology in 2020: a technology on the cusp for translation and implementation. Asia Pac J Ophthalmol (Phila) 2020; 9:61–66.
    1. Abràmoff MD, Lavin PT, Birch M, et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med 2018; 1:39.