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Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective
Open accessAbstract
The simultaneous maturation of multiple digital and telecommunications technologies in 2020 has created an unprecedented opportunity for ophthalmology to adapt to new models of care using tele-health supported by digital innovations. These digital innovations include artificial intelligence (AI), 5th generation (5G) telecommunication networks and the Internet of Things (IoT), creating an inter-dependent ecosystem offering opportunities to develop new models of eye care addressing the challenges of COVID-19 and beyond. Ophthalmology has thrived in some of these areas partly due to its many image-based investigations. Tele-health and AI provide synchronous solutions to challenges facing ophthalmologists and healthcare providers worldwide. This article reviews how countries across the world have utilised these digital innovations to tackle diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, glaucoma, refractive error correction, cataract and other anterior segment disorders. The review summarises the digital strategies that countries are developing and discusses technologies that may increasingly enter the clinical workflow and processes of ophthalmologists. Furthermore as countries around the world have initiated a series of escalating containment and mitigation measures during the COVID-19 pandemic, the delivery of eye care services globally has been significantly impacted. As ophthalmic services adapt and form a "new normal", the rapid adoption of some of telehealth and digital innovation during the pandemic is also discussed. Finally, challenges for validation and clinical implementation are considered, as well as recommendations on future directions.
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“Inaccurate and underrepresentative training data sets for AI models can cause bias, misleading predictions, adverse events, and large-scale discrimination, causing barriers to adoption [32,41,47,48].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [41]
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“Data security was identified as a major contributor toward hesitancy, with concerns around cybersecurity relating to both training and testing data as well as fear of trackers and spyware obtaining unsolicited data [39,41,42,45,48,77].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [41]
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Metadata
- Authors
- Li, J.-P. O., Liu, H., Ting, D. S. J., Jeon, S., Chan, R. V. P., Kim, J. E., Sim, D. A., Thomas, P. B. M., Lin, H., Chen, Y., Sakomoto, T., Loewenstein, A., Lam, D. S. C., Pasquale, L. R., Wong, T. Y., Lam, L. A., & Ting, D. S. W.
- Affiliations
- Moorfields Eye Hospital NHS Foundation Trust (GB)
- Beijing Tongren Hospital (CN)
- University of Nottingham (GB)
- University of Illinois Chicago (US)
- Medical College of Wisconsin (US)
- State Key Laboratory of Ophthalmology (CN)
- Peking Union Medical College Hospital (CN)
- Kagoshima University (JP)
- Tel Aviv Sourasky Medical Center (IL)
- Chinese University of Hong Kong, Shenzhen (CN)
- Icahn School of Medicine at Mount Sinai (US)
- National University of Singapore (SG)
- University of Southern California (US)
- Corresponding author
- Ting, Darren S.J.
- Ting, Daniel S.W.
- ORCID
- Venue
- Progress in Retinal and Eye Research
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 82
- Pages
- 100900
- ISSN
- 1350-9462 (ISSN-L)
- PMC
- PMC7474840
- OpenAlex
- W3083027974
- Cited by
- 626
- References
- 338
- Citations by year
- 2026: 65 · 2025: 118 · 2024: 140 · 2023: 148 · 2022: 92 · 2021: 57
- FWCI
- 12.79
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Progress in Retinal and Eye Research (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Medical Research Council (MR/T001674/1)
- Topics
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Keywords
- telemedicine, artificial intelligence, digital health, 5G, Internet of Things, ophthalmology, diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, glaucoma, refractive error correction, cataract, anterior segment disorders, COVID-19 pandemic, eye care delivery, clinical implementation, AI validation
- MeSH terms
- Digital Technology, COVID-19, SARS-CoV-2, Artificial Intelligence, Delivery of Health Care, Eye Diseases, Humans, Ophthalmology, Global Health, Telemedicine, Inventions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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