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.
“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]
In “Equity of access” (page unavailable), the paper says under-represented groups may receive inaccurate diagnoses and warns that algorithmic bias can exacerbate inequities. “Challenges in clinical deployment of AI” (page unavailable) says training datasets and validation require consideration before adoption. The copy does not specifically establish that inaccurate training data cause large-scale discrimination or adverse events. (LLM, conf=0.80)
Experimental — accuracy not yet measuredData 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.
“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]
In “Challenges in clinical deployment of AI” (page not provided), the paper identifies cybersecurity and data protection as issues for both training and testing data. The telemedicine-deployment section also lists security and privacy concerns among barriers to EHR adoption. It does not identify security as a major contributor to hesitancy or mention trackers or spyware obtaining unsolicited data. (LLM, conf=0.85)
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Self-citationIdentifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842 · see citing works
The Pathophysiology and Treatment of Glaucoma: A Review
Weinreb, R. N., Aung, T., & Medeiros, F. A. (2014) · JAMA · doi:10.1001/jama.2014.3192 · Cited by 4,210 · see citing works
Global estimates of visual impairment: 2010
Pascolini, D., & Mariotti, S. P. (2011) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2011-300539 · Cited by 3,773 · see citing works
Ranibizumab versus Verteporfin for Neovascular Age-Related Macular Degeneration
Brown, D. M., Kaiser, P. K., Michels, M., Soubrane, G., Heier, J. S., Kim, R. Y., Sy, J. P., & Schneider, S. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa062655 · Cited by 3,522 · see citing works
Flaxman, S. R., Bourne, R. R. A., Resnikoff, S., Ackland, P., Braithwaite, T., Cicinelli, M. V., Das, A., Jonas, J. B., Keeffe, J., Kempen, J. H., et al. (2017) · The Lancet Global Health · doi:10.1016/s2214-109x(17)30393-5 · Cited by 3,477 · see citing works
Self-citationNext generation 5G wireless networks: a comprehensive survey
Agiwal, M., Roy, A., & Saxena, N. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2532458 · Cited by 3,444 · see citing works
Prevalence of Diabetes among Men and Women in China
Yang, W., Lu, J., Weng, J., Jia, W., Ji, L., Xiao, J., Shan, Z., Liu, J., Tian, H., Ji, Q., et al. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa0908292 · Cited by 3,031 · see citing works
Virtually perfect? Telemedicine for COVID-19
Hollander, J. E., & Carr, B. G. (2020) · New England Journal of Medicine · doi:10.1056/nejmp2003539 · Cited by 2,970 · see citing works
The International Classification of Retinopathy of Prematurity Revisited
Prematurity*, C. O. R. O. (2005) · Archives of Ophthalmology · doi:10.1001/archopht.123.7.991 · Cited by 2,939 · see citing works
Prevalence of Age-Related Macular Degeneration in the United States
The Eye Diseases Prevalence Research Group* (2004) · Archives of Ophthalmology · doi:10.1001/archopht.122.4.564 · Cited by 2,742 · see citing works
TFOS DEWS II Epidemiology Report
Stapleton, F. J., Alves, M., Bunya, V. Y., Jalbert, I., Lekhanont, K., Malet, F., Na, K., Schaumberg, D. A., Uchino, M., Vehof, J., et al. (2017) · The Ocular Surface · doi:10.1016/j.jtos.2017.05.003 · Cited by 2,654 · see citing works
Clinically applicable deep learning for diagnosis and referral in retinal disease
De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0107-6 · Cited by 2,639 · see citing works
Self-citationTing, D. S. W., Cheung, C. Y.-L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., Hamzah, H., Garcia-Franco, R., San Yeo, I. Y., Lee, S. Y., et al. (2017) · JAMA · doi:10.1001/jama.2017.18152 · Cited by 2,402 · see citing works
Self-citationPrediction of cardiovascular risk factors from retinal fundus photographs via deep learning
Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0195-0 · Cited by 1,865 · see citing works
Prevalence and Ethnic Pattern of Diabetes and Prediabetes in China in 2013
Wang, L., Gao, P., Zhang, M., Huang, Z., Zhang, D., Deng, Q., Yichong, L., Zhao, Z., Qin, X., Jin, D., et al. (2017) · JAMA · doi:10.1001/jama.2017.7596 · Cited by 1,849 · see citing works
Causes of vision loss worldwide, 1990–2010: a systematic analysis
Bourne, R., Stevens, G. A., White, R. A., Smith, J. L., Flaxman, S. R., Price, H., Jonas, J. B., Keeffe, J. E., Leasher, J. L., Naidoo, K. S., et al. (2013) · The Lancet Global Health · doi:10.1016/s2214-109x(13)70113-x · Cited by 1,834 · see citing works
Lakhani, P., & Sundaram, B. (2017) · Radiology · doi:10.1148/radiol.2017162326 · Cited by 1,775 · see citing works
Revised Indications for the Treatment of Retinopathy of Prematurity
(2003) · Archives of Ophthalmology · doi:10.1001/archopht.121.12.1684 · Cited by 1,678 · see citing works
Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0040-6 · Cited by 1,638 · see citing works
Digital technology and COVID-19
Ting, D. S. W., Carin, L., Dzau, V., & Wong, T. Y. (2020) · Nature Medicine · doi:10.1038/s41591-020-0824-5 · Cited by 1,413 · see citing works
Self-citationPotential Biases in Machine Learning Algorithms Using Electronic Health Record Data
Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2018.3763 · Cited by 1,395 · see citing works
Gole, G. A., Ells, A. L., Katz, X., Holmström, G. E., Fielder, A. R., Capone, A., Flynn, J. T., Good, W., Holmes, J. M., McNamara, J. A., et al. (2005) · Archives of Ophthalmology · Cited by 1,386 · see citing works
Automated Identification of Diabetic Retinopathy Using Deep Learning
Gargeya, R., & Leng, T. (2017) · Ophthalmology · doi:10.1016/j.ophtha.2017.02.008 · Cited by 1,386 · see citing works
Self-management education and regular practitioner review for adults with asthma
Gibson, P. G., Powell, H. A., Wilson, A., Abramson, M. J., Haywood, P., Bauman, A. E., Hensley, M., Walters, E. H., & Roberts, J. J. (2002) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd001117 · Cited by 1,373 · see citing works
Ensuring fairness in machine learning to advance health equity.
Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018) · Annals of Internal Medicine · doi:10.7326/m18-1990 · Cited by 1,341 · see citing works
Jonas, J. B., Aung, T., Bourne, R. R., Bron, A. M., Ritch, R., & Panda-Jonas, S. (2017) · The Lancet · doi:10.1016/s0140-6736(17)31469-1 · Cited by 1,322 · see citing works
Hellström, A., Smith, L. E. H., & Dammann, O. (2013) · The Lancet · doi:10.1016/s0140-6736(13)60178-6 · Cited by 1,309 · see citing works
Cipresso, P., Giglioli, I. A. C., Alcañíz, M., & Riva, G. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.02086 · Cited by 1,136 · see citing works
Covid-19 and Health Care’s Digital Revolution
Keesara, S., Jonas, A., & Schulman, K. (2020) · New England Journal of Medicine · doi:10.1056/nejmp2005835 · Cited by 1,126 · see citing works
Abràmoff, M. D., Lou, Y., Erginay, A., Clarida, W., Amelon, R., Folk, J. C., & Niemeijer, M. (2016) · Investigative Opthalmology & Visual Science · doi:10.1167/iovs.16-19964 · Cited by 1,101 · see citing works
Ting, D. S. W., Cheung, G. C. M., & Wong, T. Y. (2016) · Clinical & Experimental Ophthalmology · doi:10.1111/ceo.12696 · Cited by 1,066 · see citing works
Self-citationFielder, A. R. (2003) · Archives of Ophthalmology · doi:10.1001/archopht.121.12.1769 · Cited by 1,002 · see citing works
Liu, Y., Wilkins, M. R., Kim, T., Malyugin, B. E., & Mehta, J. S. (2017) · The Lancet · doi:10.1016/s0140-6736(17)30544-5 · Cited by 996 · see citing works
Worldwide prevalence and risk factors for myopia
Pan, C., Ramamurthy, D., & Saw, S. (2011) · Ophthalmic and Physiological Optics · doi:10.1111/j.1475-1313.2011.00884.x · Cited by 901 · see citing works
Eubank, S., Eckstrand, I. A., Lewis, B. L., Venkatramanan, S., MARATHE, M. V., & Barrett, C. L. (2020) · Bulletin of Mathematical Biology · doi:10.1007/s11538-020-00726-x · Cited by 882 · see citing works
Video consultations for covid-19
Greenhalgh, T., Wherton, J., Shaw, S., & Morrison, C. (2020) · BMJ · doi:10.1136/bmj.m998 · Cited by 866 · see citing works
Regillo, C. D., Brown, D. M., Abraham, P., Yue, H., Ianchulev, T., Schneider, S., & Shams, N. (2008) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2007.10.004 · Cited by 858 · see citing works
Quang, D. X., & Xie, X. S. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw226 · Cited by 840 · see citing works
Li, Z., He, Y., Keel, S., Meng, W., Chang, R. T., & He, M. (2018) · Ophthalmology · doi:10.1016/j.ophtha.2018.01.023 · Cited by 830 · see citing works
SMART on FHIR: a standards-based, interoperable apps platform for electronic health records
Mandel, J. C., Kreda, D. A., Mandl, K. D., Kohane, I. S., & Ramoni, R. B. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv189 · Cited by 829 · see citing works
Gilbert, C. E. (2008) · Early Human Development · doi:10.1016/j.earlhumdev.2007.11.009 · Cited by 793 · see citing works
Artificial intelligence in retina
Schmidt-Erfurth, U., Sadeghipour, A., Gerendas, B. S., Waldstein, S. M., & Bogunović, H. (2018) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2018.07.004 · Cited by 779 · see citing works
Virtual health care in the era of COVID-19
Webster, P. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30818-7 · Cited by 769 · see citing works
Standards of medical care for type 2 diabetes in China 2019
Jia, W., Weng, J., Zhu, D., Ji, L., Lu, J., Zhou, Z., Zou, D., Guo, L., Ji, Q., Chen, L., et al. (2019) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.3158 · Cited by 703 · see citing works
Burlina, P., Joshi, N., Pekala, M. J., Pacheco, K. D., Freund, D., & Bressler, N. M. (2017) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2017.3782 · Cited by 685 · see citing works
Deep Learning Is Effective for Classifying Normal versus Age-Related Macular Degeneration OCT Images
Lee, C. S., Baughman, D. M., & Lee, A. (2017) · Ophthalmology Retina · doi:10.1016/j.oret.2016.12.009 · Cited by 659 · see citing works
Brown, J. M., Campbell, J. P., Beers, A., Chang, K., Ostmo, S., Chan, R. V. P., Dy, J., Erdogmus, D., Ioannidis, S., Kalpathy-Cramer, J., et al. (2018) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2018.1934 · Cited by 657 · see citing works
Self-citationFifteen-Year Cumulative Incidence of Age-Related Macular Degeneration
Klein, R., Klein, B. E. K., Knudtson, M. D., Meuer, S. M., Swift, M., & Gangnon, R. E. (2007) · Ophthalmology · doi:10.1016/j.ophtha.2006.10.040 · Cited by 638 · see citing works
Born Too Soon: Preterm birth matters
the Born Too Soon Preterm Birth Action Group, Howson, C. P., Kinney, M. V., McDougall, L., & Lawn, J. E. (2013) · Reproductive Health · doi:10.1186/1742-4755-10-s1-s1 · Cited by 637 · see citing works
Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning
Schlegl, T., Waldstein, S. M., Bogunović, H., Endstraßer, F., Sadeghipour, A., Philip, A.-M., Podkowinski, D., Gerendas, B. S., Langs, G., & Schmidt-Erfurth, U. M. (2017) · Ophthalmology · doi:10.1016/j.ophtha.2017.10.031 · Cited by 601 · see citing works
Krause, J., Gulshan, V., Rahimy, E., Karth, P., Widner, K., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Ophthalmology · doi:10.1016/j.ophtha.2018.01.034 · Cited by 564 · see citing works
Ung, L., Bispo, P. J. M., Shanbhag, S. S., Gilmore, M. S., & Chodosh, J. (2018) · Survey of Ophthalmology · doi:10.1016/j.survophthal.2018.12.003 · Cited by 560 · see citing works
Graßmann, F., Mengelkamp, J., Brandl, C., Harsch, S., Zimmermann, M. E., Linkohr, B., Peters, A., Heid, I. M., Palm, C., & Weber, B. H. F. (2018) · Ophthalmology · doi:10.1016/j.ophtha.2018.02.037 · Cited by 550 · see citing works
Global Prevalence of Vision Impairment and Blindness
Stevens, G. A., White, R. A., Flaxman, S. R., Price, H., Jonas, J. B., Keeffe, J. E., Leasher, J. L., Naidoo, K. S., Pesudovs, K., Resnikoff, S., et al. (2013) · Ophthalmology · doi:10.1016/j.ophtha.2013.05.025 · Cited by 539 · see citing works
Rema, M., Premkumar, S., Anitha, B., Deepa, R., Pradeepa, R., & Mohan, V. (2005) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.05-0019 · Cited by 523 · see citing works
Long-term Pattern of Progression of Myopic Maculopathy
Hayashi, K., Ohno‐Matsui, K., Shimada, N., et al. (2010) · Ophthalmology · doi:10.1016/j.ophtha.2009.11.003 · Cited by 510 · see citing works
Digital health: a path to validation
Mathews, S. C., McShea, M. J., Hanley, C. L., Ravitz, A., Labrique, A. B., & Cohen, A. B. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0111-3 · Cited by 508 · see citing works
PATHOLOGIC FINDINGS IN PATHOLOGIC MYOPIA
Grossniklaus, H. E., & GREEN, W. R. (1992) · Retina · doi:10.1097/00006982-199212020-00009 · Cited by 497 · see citing works
Wildenbos, G. A., Peute, L., & Jaspers, M. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.03.012 · Cited by 495 · see citing works
COVID-19 – the law and limits of quarantine
Parmet, W. E., & Sinha, M. S. (2020) · New England Journal of Medicine · doi:10.1056/nejmp2004211 · Cited by 492 · see citing works
Higashiyama, T., Muraki, S., & Ohji, M. (2017) · Case Reports in Ophthalmology · doi:10.1159/000461575 · Cited by 449 · see citing works
The English National Screening Programme for diabetic retinopathy 2003–2016
Scanlon, P. H. (2017) · Acta Diabetologica · doi:10.1007/s00592-017-0974-1 · Cited by 439 · see citing works
Gene expression inference with deep learning
Chen, Y., Li, Y., Narayan, R., Subramanian, A., & Xie, X. S. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw074 · Cited by 431 · see citing works
Self-citationMedia Health Literacy, eHealth Literacy, and the Role of the Social Environment in Context
Levin‐Zamir, D., & Bertschi, I. C. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15081643 · Cited by 368 · see citing works
Henderson, C., Knapp, M., Fernandez, J.-L., Beecham, J., Hirani, S. P., Cartwright, M., Rixon, L., Beynon, M., Rogers, A., Bower, P., et al. (2013) · BMJ · doi:10.1136/bmj.f1035 · Cited by 364 · see citing works
Multicenter Trial of Cryotherapy for Retinopathy of Prematurity
Cryotherapy for Retinopathy of Prematurity Cooperative Group (2001) · Archives of Ophthalmology · doi:10.1001/archopht.119.8.1110 · Cited by 364 · see citing works
Potential lost productivity resulting from the global burden of uncorrected refractive error
Smith, T. (2009) · Bulletin of the World Health Organization · doi:10.2471/blt.08.055673 · Cited by 351 · see citing works
Telemedicine During the COVID-19 Pandemic: Experiences From Western China
Hong, Z., Li, N., Li, D., Jun-hua, L., Li, B., Xiong, W., Lu, L., Li, W., & Zhou, D. (2020) · Journal of Medical Internet Research · doi:10.2196/19577 · Cited by 344 · see citing works
Song, P., Yu, J., Chan, K. Y., Τheodoratou, E., & Rudan, I. (2018) · Journal of Global Health · doi:10.7189/jogh.08.010803 · Cited by 342 · see citing works
Craig, J. E., Han, X., Qassim, A., Hassall, M., Bailey, J. N. C., Kinzy, T. G., Khawaja, A. P., An, J., Marshall, H. N., Gharahkhani, P., et al. (2020) · Nature Genetics · doi:10.1038/s41588-019-0556-y · Cited by 339 · see citing works
Self-citationBastawrous, A., Rono, H. K., Livingstone, I. A. T., Weiss, H. A., Jordan, S., Kuper, H., & Burton, M. J. (2015) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2015.1468 · Cited by 338 · see citing works
Turning the tide of corneal blindness
Oliva, M., Schottman, T., & Gulati, M. (2012) · Indian Journal of Ophthalmology · doi:10.4103/0301-4738.100540 · Cited by 332 · see citing works
Barriers for Adopting Electronic Health Records (EHRs) by Physicians
Ajami, ., & Bagheri-Tadi, T. (2013) · Acta Informatica Medica · doi:10.5455/aim.2013.21.129-134 · Cited by 319 · see citing works
Global cost of correcting vision impairment from uncorrected refractive error
Fricke, T., Holden, B., Wilson, D., et al. (2012) · Bulletin of the World Health Organization · doi:10.2471/blt.12.104034 · Cited by 310 · see citing works
Liu, H., Li, L., Wormstone, I. M., Qiao, C., Zhang, C., Liu, P., Li, S., Wang, H., Mou, D., Pang, R., et al. (2019) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2019.3501 · Cited by 307 · see citing works
Self-citationGulshan, V., Rajan, R. P., Widner, K., Wu, D., Wubbels, P., Rhodes, T., Whitehouse, K., Coram, M., Corrado, G., Ramasamy, K., et al. (2019) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2019.2004 · Cited by 303 · see citing works
Sadeh, A. D., Stolovitch, C., de Juan, E., et al. (2007) · American Academy of Pediatrics eBooks · doi:10.1542/9781581104363-ch19 · Cited by 298 · see citing works
Virtual Ophthalmology: Telemedicine in a COVID-19 Era
Saleem, S., Pasquale, L. R., Sidoti, P. A., & Tsai, J. C. (2020) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2020.04.029 · Cited by 290 · see citing works
Self-citationXie, Y., Nguyen, Q. D., Hamzah, H., Lim, G., Bellemo, V., Gunasekeran, D. V., Yip, M. Y. T., Qi Lee, X., Hsu, W., Li Lee, M., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30060-1 · Cited by 288 · see citing works
Self-citationAutomated Diabetic Retinopathy Image Assessment Software
Tufail, A., Rudisill, C., Egan, C., Kapetanakis, V. V., Salas-Vega, S., Owen, C. G., Lee, A., Louw, V., Anderson, J., Liew, G., et al. (2017) · Ophthalmology · doi:10.1016/j.ophtha.2016.11.014 · Cited by 283 · see citing works
Erratum: Corrigendum: 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/nature22985 · Cited by 279 · see citing works
Interexpert Agreement of Plus Disease Diagnosis in Retinopathy of Prematurity
Chiang, M. F. (2007) · Archives of Ophthalmology · doi:10.1001/archopht.125.7.875 · Cited by 275 · see citing works
Noah, B., Keller, M. S., Mosadeghi, S., Stein, L., Johl, S., Delshad, S., Tashjian, V. C., Lew, D., Kwan, J. T., Jusufagic, A., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-017-0002-4 · Cited by 273 · see citing works
A Treat and Extend Regimen Using Ranibizumab for Neovascular Age-Related Macular Degeneration
Gupta, O. P., Shienbaum, G., Patel, A. H., Fecarotta, C., Kaiser, R. S., & Regillo, C. D. (2010) · Ophthalmology · doi:10.1016/j.ophtha.2010.02.032 · Cited by 272 · see citing works
HEADS-UP SURGERY FOR VITREORETINAL PROCEDURES
Eckardt, C., & Paulo, E. B. (2015) · Retina · doi:10.1097/iae.0000000000000689 · Cited by 259 · see citing works
Lee, P. P. (2006) · Archives of Ophthalmology · doi:10.1001/archopht.124.1.12 · Cited by 257 · see citing works
Li, Z., Keel, S., Liu, C., He, Y., Meng, W., Scheetz, J., Lee, P. Y., Shaw, J., Ting, D., Wong, T. Y., et al. (2018) · Diabetes Care · doi:10.2337/dc18-0147 · Cited by 255 · see citing works
Self-citationMedeiros, F. A., Jammal, A. A., & Thompson, A. C. (2018) · Ophthalmology · doi:10.1016/j.ophtha.2018.12.033 · Cited by 250 · see citing works
Detecting Preperimetric Glaucoma with Standard Automated Perimetry Using a Deep Learning Classifier
Asaoka, R., Murata, H., Iwase, A., & Araie, M. (2016) · Ophthalmology · doi:10.1016/j.ophtha.2016.05.029 · Cited by 249 · see citing works
The Current State of Teleophthalmology in the United States
Rathi, S., Tsui, E. Y. K., Mehta, N., Zahid, S., & Schuman, J. S. (2017) · Ophthalmology · doi:10.1016/j.ophtha.2017.05.026 · Cited by 249 · see citing works
Development of a deep residual learning algorithm to screen for glaucoma from fundus photography
Shibata, N., Tanito, M., Mitsuhashi, K., Fujino, Y., Matsuura, M., Murata, H., & Asaoka, R. (2018) · Scientific Reports · doi:10.1038/s41598-018-33013-w · Cited by 246 · see citing works
Treder, M., Lauermann, J. L., & Eter, N. (2017) · Graefe s Archive for Clinical and Experimental Ophthalmology · doi:10.1007/s00417-017-3850-3 · Cited by 243 · see citing works
Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology
Balyen, L., & Pető, T. (2019) · Asia-Pacific Journal of Ophthalmology · doi:10.22608/apo.2018479 · Cited by 239 · see citing works
Optic Disc and Optic Cup Segmentation Methodologies for Glaucoma Image Detection: A Survey
Almazroa, A. A., Burman, R., Raahemifar, K., et al. (2015) · Journal of Ophthalmology · doi:10.1155/2015/180972 · Cited by 235 · see citing works
Ruamviboonsuk, P., Krause, J., Chotcomwongse, P., Sayres, R., Raman, R., Widner, K., Campana, B. J. L., Phene, S., Hemarat, K., Tadarati, M., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0099-8 · Cited by 233 · see citing works
Hassan, M., Fuchs, T. J., Cuir, N. D., Moraes, C. G. D., Blumberg, D. M., Liebmann, J. M., Ritch, R. H., & Hood, D. C. (2017) · Journal of Glaucoma · doi:10.1097/ijg.0000000000000765 · Cited by 228 · see citing works
Comparison of machine learning and traditional classifiers in glaucoma diagnosis
Chan, K., Lee, T.-W., Sample, P. A., Goldbaum, M. H., Weinreb, R. N., & B, T. J. S. A. (2002) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2002.802012 · Cited by 227 · see citing works
Philip, S., Fleming, A., Goatman, K. A., Fonseca, S., McNamee, P., Scotland, G., Prescott, G. J., Sharp, P. F., & Olson, J. A. (2007) · British Journal of Ophthalmology · doi:10.1136/bjo.2007.119453 · Cited by 225 · see citing works
Aanestad, M., & Jensen, T. B. (2011) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2011.03.006 · Cited by 221 · see citing works
ULTRA-WIDEFIELD FUNDUS IMAGING
Nagiel, A., Lalane, R. A., Sadda, S. R., et al. (2016) · Retina · doi:10.1097/iae.0000000000000937 · Cited by 221 · see citing works
Rao, G. N., Khanna, R., Athota, S. M., Rajshekar, V., & Rani, P. (2012) · Indian Journal of Ophthalmology · doi:10.4103/0301-4738.100533 · Cited by 219 · see citing works
Cost-effectiveness of a National Telemedicine Diabetic Retinopathy Screening Program in Singapore
Nguyen, H. V., Tan, G. S. W., Tapp, R. J., Mital, S., Ting, D. S. W., Wong, H. T., Tan, C. S., Laude, A., Tai, E. S., Tan, N. C., et al. (2016) · Ophthalmology · doi:10.1016/j.ophtha.2016.08.021 · Cited by 218 · see citing works
Self-citationSingh, A., Dutta, M. K., Mangipudi, P., Uher, V., & Bürget, R. (2015) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.10.010 · Cited by 213 · see citing works
Simkó, M., & Mattsson, M.-O. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16183406 · Cited by 209 · see citing works
Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology
Ting, D. S. J., Foo, V. H. X., Yang, L. W. Y., Sia, J. T., Ang, M., Lin, H., Chodosh, J., Mehta, J. S., & Ting, D. S. W. (2020) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2019-315651 · Cited by 205 · see citing works
Self-citationA Deep Learning Framework for Predicting Response to Therapy in Cancer
Sakellaropoulos, T., Vougas, K. N., Narang, S., Koinis, F., Kotsinas, A., Polyzos, A., Moss, T. J., Piha-Paul, S. A., Zhou, H., Kardala, E., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.11.017 · Cited by 204 · see citing works
Issac, A., Sarathi, M. P., & Dutta, M. K. (2015) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.08.002 · Cited by 203 · see citing works
Burgansky–Eliash, Z., Wollstein, G., Chu, T., Ramsey, J., Glymour, C., Noecker, R. J., Ishikawa, H., & Schuman, J. S. (2005) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.05-0366 · Cited by 201 · see citing works
Schmidt-Erfurth, U. M., Bogunović, H., Sadeghipour, A., Schlegl, T., Langs, G., Gerendas, B. S., Osborne, A. R., & Waldstein, S. M. (2017) · Ophthalmology Retina · doi:10.1016/j.oret.2017.03.015 · Cited by 200 · see citing works
Deep Learning for Predicting Refractive Error From Retinal Fundus Images
Varadarajan, A. V., Poplin, R., Blumer, K., Angermueller, C., Ledsam, J., Chopra, R., Keane, P. A., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.18-23887 · Cited by 200 · see citing works
Healthy China 2030: moving from blueprint to action with a new focus on public health
Chen, P., Li, F., & Harmer, P. (2019) · The Lancet Public Health · doi:10.1016/s2468-2667(19)30160-4 · Cited by 198 · see citing works
Recent Developments in Clinical Terminologies — SNOMED CT, LOINC, and RxNorm
Bodenreider, O., Cornet, R., & Vreeman, D. J. (2018) · Yearbook of Medical Informatics · doi:10.1055/s-0038-1667077 · Cited by 193 · see citing works
Chew, E. Y., Clemons, T. E., Bressler, S. B., Elman, M. J., Danis, R. P., Domalpally, A., Heier, J. S., Kim, J. E., & Garfinkel, R. A. (2013) · Ophthalmology · doi:10.1016/j.ophtha.2013.10.027 · Cited by 192 · see citing works
Self-citationDiode Laser Photocoagulation for Threshold Retinopathy of Prematurity
Hunter, D. G., & Repka, M. X. (1993) · Ophthalmology · doi:10.1016/s0161-6420(93)31664-7 · Cited by 189 · see citing works
Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review
Haleem, M. S., Han, L., Hemert, J. I. V., & Li, B. (2013) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2013.09.005 · Cited by 186 · see citing works
Laser Photocoagulation for Stage 3+ Retinopathy of Prematurity
McNamara, J. A., Tasman, W. S., Brown, G. C., et al. (1991) · Ophthalmology · doi:10.1016/s0161-6420(91)32247-4 · Cited by 185 · see citing works
Redd, T. K., Campbell, J. P., Brown, J. M., Kim, S. J., Ostmo, S. R., Chan, R. V. P., Dy, J., Deniz Erdogmus, B., Ioannidis, S., Kalpathy–Cramer, J., et al. (2018) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2018-313156 · Cited by 182 · see citing works
Self-citationChristopher, M. A., Bowd, C., Belghith, A., Goldbaum, M. H., Weinreb, R. N., Fazio, M. A., Girkin, C. A., Liebmann, J. M., & Zangwill, L. M. (2019) · Ophthalmology · doi:10.1016/j.ophtha.2019.09.036 · Cited by 181 · see citing works
Schwartz, S. D., Harrison, S. A., FERRONE, P. J., et al. (2000) · Ophthalmology · doi:10.1016/s0161-6420(99)00003-2 · Cited by 179 · see citing works
Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application
Bellemo, V., Lim, G., Rim, T. H., Tan, G. S. W., Cheung, C. Y.-L., Sadda, S. R., He, M., Tufail, A., Lee, M. L., Hsu, W., et al. (2019) · Current Diabetes Reports · doi:10.1007/s11892-019-1189-3 · Cited by 177 · see citing works
Self-citationDeep learning models in genomics; are we there yet?
Koumakis, L. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.06.017 · Cited by 177 · see citing works
Deep Learning for Predicting Refractive Error From Retinal Fundus Images
Varadarajan, A., Poplin, R., Blumer, K., Angermueller, C., Ledsam, J., Chopra, R., Keane, P. A., Corrado, G., Peng, L., & Webster, D. (2018) · UCL Discovery (University College London) · Cited by 174 · see citing works
Validation of Smartphone Based Retinal Photography for Diabetic Retinopathy Screening
Rajalakshmi, R., Arulmalar, S., Usha, M., Prathiba, V., Kareemuddin, K. S., Anjana, R. M., & Mohan, V. (2015) · PLOS ONE · doi:10.1371/journal.pone.0138285 · Cited by 173 · see citing works
Lin, H., Long, E., Ding, X., et al. (2018) · PLoS Medicine · doi:10.1371/journal.pmed.1002674 · Cited by 173 · see citing works
Self-citationCosts of Refractive Correction of Distance Vision Impairment in the United States, 1999–2002
Vitale, S., Cotch, M. F., Sperduto, R. D., et al. (2006) · Ophthalmology · doi:10.1016/j.ophtha.2006.06.033 · Cited by 172 · see citing works
Wallace, D. K., Quinn, G. E., Freedman, S. F., et al. (2008) · Journal of American Association for Pediatric Ophthalmology and Strabismus · doi:10.1016/j.jaapos.2007.11.022 · Cited by 166 · see citing works
Prevalence of Primary Open Angle Glaucoma in a Rural Adult Chinese Population: The Handan Eye Study
Liang, Y., Friedman, D. S., Zhou, Q., et al. (2011) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.11-7472 · Cited by 164 · see citing works
Foot, B., & MacEwen, C. J. (2017) · Eye · doi:10.1038/eye.2017.1 · Cited by 160 · see citing works
A Universal Artificial Intelligence Platform for Collaborative Management of Cataracts
Wu, X., Huang, Y., Liu, Z., Lai, W., Long, E., Zhang, K., Jiang, J., Lin, D., Chen, K., Wu, D., et al. (2019) · The Lancet · doi:10.1016/s0140-6736(19)32358-x · Cited by 159 · see citing works
Self-citationKırkızlar, E., Serban, N., Sisson, J. A., Swann, J., Barnes, C. S., & Williams, M. D. (2013) · Ophthalmology · doi:10.1016/j.ophtha.2013.06.029 · Cited by 148 · see citing works
Bogunović, H., Montuoro, A., Baratsits, M., Karantonis, M. G., Waldstein, S. M., Schlanitz, F. G., & Schmidt-Erfurth, U. M. (2017) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.17-21789 · Cited by 143 · see citing works
Teleophthalmology: improving patient outcomes?
Sreelatha, O. K., & Ve, R. S. (2016) · Clinical ophthalmology · doi:10.2147/opth.s80487 · Cited by 136 · see citing works
Tufail, A., Kapetanakis, V. V., Salas-Vega, S., Egan, C. A., Rudisill, C., Owen, C. G., Lee, A., Louw, V., Anderson, J., Liew, G., et al. (2016) · Health Technology Assessment · doi:10.3310/hta20920 · Cited by 134 · see citing works
Scanlon, P. H. (2008) · Journal of Medical Screening · doi:10.1258/jms.2008.008015 · Cited by 133 · see citing works
Promising Artificial Intelligence–Machine Learning–Deep Learning Algorithms in Ophthalmology
(2019) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/01.apo.0000586388.81551.d0 · Cited by 132 · see citing works
Associations between Metamorphopsia and Foveal Microstructure in Patients with Epiretinal Membrane
Okamoto, F., Sugiura, Y., Okamoto, Y., et al. (2012) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.12-9683 · Cited by 130 · see citing works
Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry.
Goldbaum, M. H., Sample, P. A., Chan, K., et al. (2002) · PubMed · Cited by 125 · see citing works
Li, F., Wang, Z., Qu, G., et al. (2018) · BMC Medical Imaging · doi:10.1186/s12880-018-0273-5 · Cited by 125 · see citing works
Accuracy and Reliability of Remote Retinopathy of Prematurity Diagnosis
Chiang, M. F. (2006) · Archives of Ophthalmology · doi:10.1001/archopht.124.3.322 · Cited by 124 · see citing works
The Relationship between Variability and Sensitivity in Large-Scale Longitudinal Visual Field Data
Russell, R. A., Crabb, D. P., Malik, R., et al. (2012) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.12-10428 · Cited by 123 · see citing works
Loewenstein, A., Malach, R., Goldstein, M., Leibovitch, I., Barak, A., Baruch, E., Alster, Y., Rafaeli, O., Avni, I., & Yassur, Y. (2003) · Ophthalmology · doi:10.1016/s0161-6420(03)00074-5 · Cited by 123 · see citing works
Self-citationDetection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning
Yousefi, S., Kiwaki, T., Zheng, Y., et al. (2018) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2018.06.007 · Cited by 121 · see citing works
Expert Diagnosis of Plus Disease in Retinopathy of Prematurity From Computer-Based Image Analysis
Campbell, J. P., Ataer-Cansızoğlu, E., Bolón‐Canedo, V., Bozkurt, A., Deniz Erdogmus, B., Kalpathy–Cramer, J., PATEL, S. N., Reynolds, J. D. H., Horowitz, J., Hutcheson, K. A., et al. (2016) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2016.0611 · Cited by 119 · see citing works
Self-citationScreening for retinopathy of prematurity employing the retcam 120: sensitivity and specificity.
Roth, D. B., Morales, D. S., Feuer, W. J., Hess, D. J., Johnson, R. A., & Flynn, J. T. (2001) · PubMed · Cited by 118 · see citing works
Wang, M., Shen, L. Q., Pasquale, L. R., Petrakos, P., Formica, S., Boland, M. V., Wellik, S. R., Moraes, C. G. D., Myers, J. S., Saeedi, O. J., et al. (2019) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.18-25568 · Cited by 118 · see citing works
Self-citationInterpretation of automated perimetry for glaucoma by neural network.
Goldbaum, M. H., Sample, P. A., White, H., Côlt, B., Raphaelian, P., Fechtner, R. D., & Weinreb, R. N. (1994) · PubMed · Cited by 111 · see citing works
Devalla, S. K., Chin, K. S., Mari, J. M., Tun, T. A., Strouthidis, N. G., Aung, T., Thiéry, A. H., & Girard, M. J. A. (2018) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.17-22617 · Cited by 110 · see citing works
Kaptein, A. A., Fischer, M. J., & Scharloo, M. (2014) · International Journal of COPD · doi:10.2147/copd.s49622 · Cited by 107 · see citing works
Moyer, V. A. (2013) · Annals of Internal Medicine · doi:10.7326/0003-4819-159-5-201309030-00008 · Cited by 103 · see citing works
Prevalence of Glaucoma in a Rural Northern China Adult Population
Song, W., Li, S., Cheng, F., et al. (2011) · Ophthalmology · doi:10.1016/j.ophtha.2011.02.050 · Cited by 103 · see citing works
Wickham, L., Hay, G. R., HAMILTON, R. D., Wooding, J., Tossounis, H., da Cruz, L., Siriwardena, D., & Strouthidis, N. G. (2020) · Eye · doi:10.1038/s41433-020-0957-2 · Cited by 103 · see citing works
Chen, Y. (2006) · British Journal of Ophthalmology · doi:10.1136/bjo.2005.078063 · Cited by 102 · see citing works
Self-citationInomata, T., Iwagami, M., Nakamura, M., Shiang, T., Yoshimura, Y., Fujimoto, K., Okumura, Y., Eguchi, A., Iwata, N., Miura, M., et al. (2019) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2019.4815 · Cited by 102 · see citing works
Telemedicine and e-Health Solutions for COVID-19: Patients' Perspective
Pappot, N., Taarnhøj, G. A., & Pappot, H. (2020) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2020.0099 · Cited by 100 · see citing works
CAVALLERANO, A. A., Cavallerano, J. D., Katalinic, P., et al. (2005) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2004.10.064 · Cited by 99 · see citing works
Strategies for improving early detection of glaucoma: the combined structure–function index
Medeiros, F. A., Tatham, A. J., & Weinreb, R. N. (2014) · Clinical ophthalmology · doi:10.2147/opth.s44586 · Cited by 98 · see citing works
Ting, D. S. W., Lin, H., Ruamviboonsuk, P., Wong, T. Y., & Sim, D. A. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30217-1 · Cited by 98 · see citing works
Self-citationLi, Z., Guo, C., Nie, D., Lin, D., Zhu, Y., Chen, C., Wu, X., Xu, F., Jin, C., Zhang, X., et al. (2020) · Communications Biology · doi:10.1038/s42003-019-0730-x · Cited by 98 · see citing works
Self-citationKern, C., Fu, D. J., Kortuem, K., Huemer, J., Barker, D., Davis, A., Balaskas, K., Keane, P. A., McKinnon, T., & Sim, D. A. (2020) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2019-314161 · Cited by 97 · see citing works
Self-citationVariation in Electronic Health Record Adoption and Readiness for Meaningful Use: 2008–2011
Patel, V., Jamoom, E. W., Hsiao, C., Furukawa, M. F., & Buntin, M. J. B. (2013) · Journal of General Internal Medicine · doi:10.1007/s11606-012-2324-x · Cited by 95 · see citing works
Changing challenges in the control of blindness in children
Gilbert, C. E. (2007) · Eye · doi:10.1038/sj.eye.6702841 · Cited by 89 · see citing works
Callaway, N. F., Luo, Z., Henderson, M. T., et al. (2014) · Ophthalmic surgery, lasers & imaging retina · doi:10.3928/23258160-20140122-01 · Cited by 89 · see citing works
Scotland, G., McNamee, P., Fleming, A., Goatman, K. A., Philip, S., Prescott, G. J., Sharp, P. F., Williams, G. J., Wykes, W. N., Leese, G., et al. (2009) · British Journal of Ophthalmology · doi:10.1136/bjo.2008.151126 · Cited by 89 · see citing works
The glaucoma book : a practical, evidence-based approach to patient care
Schacknow, P. N., & Samples, J. R. (2010) · DIAL (Catholic University of Leuven) · doi:10.1007/978-0-387-76700-0 · Cited by 88 · see citing works
Ophthalmologists Are More Than Eye Doctors—In Memoriam Li Wenliang
Parrish, R. K., II, Stewart, M. W., & Powers, S. L. D. (2020) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2020.02.014 · Cited by 88 · see citing works
Neural Networks to Identify Glaucoma With Structural and Functional Measurements
Brigatti, L., Hoffman, D. C., & Caprioli, J. (1996) · American Journal of Ophthalmology · doi:10.1016/s0002-9394(14)75425-x · Cited by 87 · see citing works
Dhaliwal, C. A., Wright, E., Graham, C., et al. (2008) · British Journal of Ophthalmology · doi:10.1136/bjo.2008.148908 · Cited by 87 · see citing works
Integrating holistic and local deep features for glaucoma classification
Li, A., Cheng, J. T., Wong, D. W. K., & Liu, J. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7590952 · Cited by 87 · see citing works
Can Home Monitoring Allow Earlier Detection of Rapid Visual Field Progression in Glaucoma?
Anderson, A. J., Bedggood, P. A., Kong, Y. X. G., et al. (2017) · Ophthalmology · doi:10.1016/j.ophtha.2017.06.028 · Cited by 87 · see citing works
Plus Disease in Retinopathy of Prematurity
Kalpathy–Cramer, J., Campbell, J. P., Deniz Erdogmus, B., Tian, P., Kedarisetti, D., Moleta, C., Reynolds, J. D. H., Hutcheson, K. A., Shapiro, M. J., Repka, M. X., et al. (2016) · Ophthalmology · doi:10.1016/j.ophtha.2016.07.020 · Cited by 86 · see citing works
Self-citationVirtual clinics in glaucoma care: face-to-face versus remote decision-making
Clarke, J. C., Puertas, R., Kotecha, A., et al. (2016) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2016-308993 · Cited by 85 · see citing works
Keel, S., Li, Z., Scheetz, J., Robman, L. D., Phung, J., Makeyeva, G. A., Aung, K.-Z., Liu, C., Yan, X., Meng, W., et al. (2019) · Clinical and Experimental Ophthalmology · doi:10.1111/ceo.13575 · Cited by 85 · see citing works
The War on Diabetic Retinopathy: Where Are We Now?
Wong, T. Y., & Sabanayagam, C. (2019) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000267 · Cited by 84 · see citing works
Self-citationPractice Guidelines for Ocular Telehealth-Diabetic Retinopathy, Third Edition
Horton, M. B., Brady, C. J., Cavallerano, J. D., Abràmoff, M. D., Barker, G. P., Chiang, M. F., Crockett, C. H., Garg, S., Karth, P. A., Liu, Y., et al. (2020) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2020.0006 · Cited by 84 · see citing works
Oculoplastic video-based telemedicine consultations: Covid-19 and beyond
Kang, S., Thomas, P. B. M., Sim, D. A., et al. (2020) · Eye · doi:10.1038/s41433-020-0953-6 · Cited by 83 · see citing works
Self-citationTing, D. S. W., Cheung, C. Y.-L., Nguyen, Q., Sabanayagam, C., Lim, G., Lim, Z. W., Tan, G. S. W., Soh, Y. Q., Schmetterer, L. F., Wang, Y. X., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0097-x · Cited by 82 · see citing works
Self-citationArtificial Intelligence in Diabetic Eye Disease Screening
(2019) · Asia-Pacific Journal of Ophthalmology · doi:10.22608/apo.201976 · Cited by 82 · see citing works
Fleming, C. (2005) · The Annals of Family Medicine · doi:10.1370/afm.293 · Cited by 81 · see citing works
Chiang, M. F., Melia, M., Buffenn, A. N., et al. (2012) · Ophthalmology · doi:10.1016/j.ophtha.2012.01.002 · Cited by 80 · see citing works
Laser Therapy for Retinopathy of Prematurity
(1994) · Archives of Ophthalmology · doi:10.1001/archopht.1994.01090140028007 · Cited by 80 · see citing works
L'Examen qualitatif de la fonction maculaire
Amsler, M. (2010) · Ophthalmologica · doi:10.1159/000300476 · Cited by 77 · see citing works
Azuara‐Blanco, A., Burr, J. M., Thomas, R., MacLennan, G., & McPherson, S. (2007) · British Journal of Ophthalmology · doi:10.1136/bjo.2007.119628 · Cited by 77 · see citing works
Early Impact Of CMS Expansion Of Medicare Telehealth During COVID-19
(2020) · Forefront Group · doi:10.1377/forefront.20200715.454789 · Cited by 77 · see citing works
Wright, H. R., & Diamond, J. P. (2014) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2014-305588 · Cited by 76 · see citing works
Khan, T., Bertram, M. Y., Jina, R., Mash, B., Levitt, N., & Hofman, K. (2013) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2013.05.006 · Cited by 74 · see citing works
Telemedicine screening of retinal diseases with a handheld portable non-mydriatic fundus camera
Jin, K., Lu, H., Su, Z., et al. (2017) · BMC Ophthalmology · doi:10.1186/s12886-017-0484-5 · Cited by 74 · see citing works
Li, B., Powell, A.-M., Hooper, P. L., & Sheidow, T. (2014) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2014.5014 · Cited by 73 · see citing works
Experiences with developing and implementing a virtual clinic for glaucoma care in an NHS setting
Kotecha, A., Brookes, J. L., Foster, P. J., & Baldwin, A. (2015) · Clinical ophthalmology · doi:10.2147/opth.s92409 · Cited by 73 · see citing works
Bourdon, H., Jaillant, R., Ballino, A., et al. (2020) · Journal Français d Ophtalmologie · doi:10.1016/j.jfo.2020.05.005 · Cited by 73 · see citing works
Medicare Costs for Neovascular Age-Related Macular Degeneration, 1994–2007
Day, S., Acquah, J., Lee, P. P., Mruthyunjaya, P., & Sloan, F. A. (2011) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2011.05.008 · Cited by 72 · see citing works
Flagged first, then by citations
Research works printed but not linked: 7
- Abràmoff Michael D. Lavin Philip T. Birch Michele Shah Nilay James C Folk Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices NPJ Digit. Med. 1 2018 39 31304320 PMC6550188
- American Medical Association Augmented intelligence in health care. Policy 2019 https://www.ama-assn.org/system/files/2019-08/ai-2018-board-policy-summary.pdf
- Masumoto Hiroki Tabuchi Hitoshi Nakakura Shunsuke Ishitobi Naofumi Miki Masayuki Enno Hiroki Deep-learning classifier with an ultrawide-field scanning laser ophthalmoscope detects glaucoma visual field severity J. Glaucoma 27 2018 647 652 29781835
- National Academies of Sciences, Engineering, Medicine, Policy, Affairs Global, Roundtable Government-University-Industry Research The national Academies collection: reports funded by national institutes of health Saunders J. The Transformational Impact of 5G: Proceedings of a Workshop-In Brief 2019 National Academies Press (US) Copyright 2019 By the National Academy of Sciences. All Rights Reserved.: Washington (DC))
- Nordrum A. Clark K. IEEE Spectrum staff '5G Bytes: Small Cells Explained 2017 IEEE Spectrum Accessed 20 February 2020 https://spectrum.ieee.org/video/telecom/wireless/5g-bytes-small-cells-explained
- Samuel A.L. 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 2000 207 226
- World Health Organisation WHO Guideline: Recommendations on Digital Interventions for Health System Strengthening 2019 31162915
Cited by
Sorted by citations
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 · see citing works
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 324 · see citing works
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 · see citing works
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 · see citing works
Self-citationArtificial intelligence for biomarker discovery in Alzheimer's disease and dementia
Winchester, L. M., Harshfield, E. L., Shi, L., Badhwar, A., Khleifat, A. A., Clarke, N., Dehsarvi, A., Lengyel, I., Lourida, I., Madan, C. R., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13390 · Cited by 130 · see citing works
Challenges of artificial intelligence in medicine and dermatology
Grzybowski, A., Jin, K., & Wu, H. (2024) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2023.12.013 · Cited by 115 · see citing works
González-Gonzalo, C., Thee, E. F., Klaver, C. C. W., Lee, A. Y., Schlingemann, R. O., Tufail, A., Verbraak, F., & Sánchez, C. I. (2022) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2021.101034 · Cited by 103 · see citing works
Gao, Z., Pan, X., Shao, J., Jiang, X., Su, Z., Jin, K., & Ye, J. (2023) · British Journal of Ophthalmology · doi:10.1136/bjo-2022-321472 · Cited by 93 · see citing works
Image Encryption Algorithm Combining Chaotic Image Encryption and Convolutional Neural Network
Feng, L., Du, J., Fu, C., & Song, W. (2023) · Electronics · doi:10.3390/electronics12163455 · Cited by 58 · see citing works
Pereira, C. S., Veloso, B., Durão, N., & Moreira, F. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2021.11.071 · Cited by 46 · see citing works
- 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 · 2020: 5
- 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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