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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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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
Saeedi, P., Petersohn, I., Salpea, P., Malanda, B., Karuranga, S., Unwin, N., Colagiuri, S., Guariguata, L., Motala, A. A., Ogurtsova, K., et al. (2019) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2019.107843 · Cited by 10,792
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697
Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040
Tham, Y.-C., Li, X., Wong, T. Y., Quigley, H. A., Aung, T., & Cheng, C.-Y. (2014) · Ophthalmology · doi:10.1016/j.ophtha.2014.05.013 · Cited by 7,159
Self-citationVos, T., Barber, R. M., Bell, B., Bertozzi-Villa, A., Biryukov, S., Bolliger, I., Charlson, F., Davis, A., Degenhardt, L., Dicker, D., et al. (2015) · The Lancet · doi:10.1016/s0140-6736(15)60692-4 · Cited by 6,414
Self-citationRanibizumab for Neovascular Age-Related Macular Degeneration
Rosenfeld, P. J., Brown, D. M., Heier, J. S., Boyer, D. S., Kaiser, P. K., Chung, C. Y., & Kim, R. Y. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa054481 · Cited by 5,886
Global prevalence and major risk factors of diabetic retinopathy
Yau, J. W. Y., Rogers, S. L., Kawasaki, R., Lamoureux, E. L., Kowalski, J. W., Bek, T., Chen, S.-J., Dekker, J. M., Fletcher, A., Grauslund, J., et al. (2012) · Diabetes Care · doi:10.2337/dc11-1909 · Cited by 4,985
Self-citation
Research works printed but not linked: 5
- Abràmoff, 2018, Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices, NPJ Digit. Med., 1, 39, 10.1038/s41746-018-0040-6 · DOI · Paper page
- Horton, 2020, vol. 26, 495
- Masumoto, 2018, Deep-learning classifier with an ultrawide-field scanning laser ophthalmoscope detects glaucoma visual field severity, J. Glaucoma, 27, 647, 10.1097/IJG.0000000000000988 · DOI
- McCarthy, 2006, A proposal for the Dartmouth summer research project on artificial intelligence - August 31, 1955, AI Mag., 27, 12
- Samuel, 2000, Some studies in machine learning using the game of checkers (Reprinted from Journal of Research and Development, vol 3, 1959, IBM J. Res. Dev., 44, 207
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Sorted by citations
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Stoumpos, A. I., Kitsios, F., & Talias, M. A. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20043407 · Cited by 939
Economics of artificial intelligence in healthcare: Diagnosis vs. treatment
Khanna, N. N., Maindarkar, M. A., Viswanathan, V., Fernandes, J. F. E., Paul, S., Bhagawati, M., Ahluwalia, P., Ruzsa, Z., Sharma, A., Kolluri, R., et al. (2022) · Healthcare · doi:10.3390/healthcare10122493 · Cited by 347
Barriers to and facilitators of artificial intelligence adoption in health care: scoping review
Hassan, M., Kushniruk, A., & Borycki, E. (2024) · JMIR Human Factors · doi:10.2196/48633 · Cited by 435
A generalist medical language model for disease diagnosis assistance
Liu, X., Liu, H., Yang, G., Jiang, Z., Cui, S., Zhang, Z., Wang, H., Tao, L., Sun, Y., Song, Z., et al. (2025) · Nature Medicine · doi:10.1038/s41591-024-03416-6 · Cited by 183
Self-citationLarge language models and their impact in ophthalmology
Betzler, B. K., Chen, H., Cheng, C.-Y., Lee, C. S., Ning, G., Song, S. J., Lee, A. Y., Kawasaki, R., van Wijngaarden, P., Grzybowski, A., et al. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00201-7 · Cited by 161
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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
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