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Review Article| Volume 3, ISSUE 1, P21-38, August 2018

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Artificial Intelligence and Its Applications in Vision and Eye Care

      A complete description of artificial intelligence (AI) and neural networking with general examples of machine and deep learning and a specific neural network example of human vision.

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      References

        • Purkayastha P.
        Artificial intelligence and the threat to humanity.
        NewsClick. 2017;
        • Lewis T.
        A brief history of artificial intelligence.
        Live Science. 2014;
        • Ulanoff L.
        Bill gates: AI is the holy grail.
        Mashable Tech. 2016;
        • Marr B.
        28 best quotes about artificial intelligence.
        Forbes. 2017;
        • Kiron D.
        What managers need to know about artificial intelligence.
        MIT Sloan Management Review. 2017;
        • Parker C.B.
        One hundred year study on artificial intelligence (AI100).
        Stanford News Service. 2016;
        • Desouza K.
        • Krishnamurthy R.
        • Dawson G.S.
        Learning from public sector experimentation with artificial intelligence.
        Brookings Institute Think Tank. 2017;
        • Brynjolfsson E.
        • Mcafee A.
        The business of artificial intelligence.
        Harvard Business Review (Cover story). 2017;
        • Columbus L.
        How artificial intelligence is revolutionizing business In 2017.
        Forbes Tech Big Data. 2017;
        • Lewis-Kraus G.
        The great AI awakening.
        New York Times Magazine. 2016;
      1. Available at: https://www.predictiveanalyticstoday.com/artificial-intelligence-platforms/. Accessed December 1, 2017.

        • Litjens G.
        • Kooi T.
        • Bejnordi B.E.
        • et al.
        A survey on deep learning in medical image analysis.
        Med Image Anal. 2017; 42: 60-88
      2. "Graphics Processing Unit (GPU)". Nvidia. Available at: https://www.geforce.com/hardware. Accessed March 29, 2016.

        • Gallo A.
        A refresher on regression analysis.
        Harvard Business Review No. 4. 2015;
        • Lin H.
        Chapter 11 – bridging the logic-based and probability-based approaches to artificial intelligence.
        Academic Press, Cambridge (MA)2017: 215-225https://doi.org/10.1016/B978-0-12-804600-5.00011-8
        • Suchow J.W.
        • Bourgin D.D.
        • Griffiths T.L.
        Evolution in mind: evolutionary dynamics, cognitive processes, and bayesian inference. ScienceDirect. 21 (7). Elsevier, Cambridge (MA)2017: 522-530
      3. ArseneIoan O, DumitracheIoana M. Expert system for medicine diagnosis using software agents. Science Direct https://doi.org/10.1016/j.eswa.2014.10.026.

        • LeCun Y.
        • Bengio Y.
        • Hinton G.
        Deep learning.
        Nature. 2015; 521: 436-444
        • Liang P.
        Semi-supervised learning for natural language.
        MIT Press, Cambridge (MA)2005: 44-52
        • Sutton R.S.
        • Barto A.G.
        Reinforcement learning an introduction.
        MIT Press, Cambridge (MA)2012
        • Fan S.
        Google’s new ai gets smarter thanks to a working memory.
        Singularity Hub. 2016;
        • Garvert M.M.
        • Frston K.J.
        • Dolan R.J.
        • et al.
        Subcortical amygdala pathways enable rapid face processing. ScienceDirect. 102 (Part 2). Elsevier, Cambridge (MA)2014: 309-316
        • Mujica-Parodi L.R.
        • Jiook Cha J.
        • Gao J.
        From anxious to reckless: a control systems approach unifies prefrontal-limbic regulation across the spectrum of threat detection.
        Front Syst NeuroScience. 2017; 11: 18
      4. Ba J, Mnih V, Kavukcuoglu K. Multiple object recognition with visual attention. In Proc. International Conference on Learning Representations. 2014. Available at: http://arxiv.org/abs/1412.7755. Accessed December 24, 2014.

        • Mnih V.
        • Kavukcuoglu K.
        • Silver D.
        • et al.
        Human-level control through deep reinforcement learning.
        Nature. 2015; 518: 529-533
      5. Situmorang BH, Setiawan MP, Tosida ET. Decision support system for determining the contact lens for refractive errors patients with classification ID3. IOP Conference Series: Materials Science and Engineering Volume 166, Conference 1, held at Bogor, Indonesia, August 27, 2016.

        • Legras R.
        • Chateau N.
        • Charman W.N.
        Assessment of just-noticeable differences for refractive errors and spherical aberration using visual simulation.
        Optom Vis Sci. 2004; 81: 718-728
        • VanRullen R.
        Perception science in the age of deep neural networks.
        Front Psychol. 2017; 8: 2017
        • Bruce A.S.
        • Catania L.J.
        Clinical applications of wavefront refraction.
        Optom Vis Sci. 2014; 91: 1278-1286
        • Bresnick J.
        Microsoft takes on blindness, eye care with AI, machine learning.
        Health IT Analytics. 2016;
        • Naidoo K.
        Applying AI to help millions of visually impaired across the world.
        Brien Holden Institute. 2016;
        • Pasupuleti G.
        Democratizing eyecare in india & globally using artificial intelligence to combat blindness.
        Breathe Publications. 2016;
      6. Available at: http://www.ncbi.nlm.nih.gov/pubmed/12888056. Accessed August 1, 2003.

        • Vidya K.
        • Sudarshan V.K.
        • Joel E.W.
        • et al.
        Evaluation of evaporative dry eye disease using thermal images of ocular surface regions with DWT and gabor transform.
        Appl Infrared Biomed Sci. 2017; (Online): 359-375
        • Long E.
        • Lin H.
        • Liu Z.
        An artificial intelligence platform for the multihospital collaborative management of congenital cataracts.
        Nat Biomed Eng. 2017; 1: 0024
      7. Available at: http://www.londoneyehospital.com/treatments-services/verion-surgery. Accessed August 9, 2017.

        • González-López J.J.
        • García-Aparicio A.M.
        • Sánchez-Ponce D.
        Development and validation of a Bayesian network for the differential diagnosis of anterior uveitis.
        Eye (Lond). 2016; 30: 865-872
        • Janakiram M.S.V.
        Google's research in artificial intelligence helps in preventing blindness caused by diabetes.
        Forbes Tech. 2017;
        • Condliffe J.
        DeepMind’s first medical research gig will use AI to diagnose eye disease.
        MIT Technology Review. 2016;
        • Gulshan V.
        • Peng L.
        • Coram M.
        • et al.
        Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.
        JAMA. 2016; 316: 2402-2410
        • Schmidt-Erfurth U.
        • Bogunovic H.
        • Sadeghipour A.
        • et al.
        Machine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degeneration.
        Ophthalmol Retina. 2017; (Online)
        • Harrison L.
        Artificial intelligence will bring ‘reality’ to glaucoma diagnosis.
        Ophthalmol Times. 2017;
        • Modi S.
        Artificial intelligence and neurology.
        J Biomed Syst Emerg Technol. 2016; 4: 112
        • Senders J.T.
        • Arnaout S.O.
        • Karhade A.V.
        • et al.
        Natural and artificial intelligence in neurosurgery: a systematic review.
        Neurosurgery. 2017; ([Epub ahead of print])
        • He K.Y.
        • Ge D.
        • He M.M.
        • et al.
        Big data analytics for genomic medicine. Artificial intelligence in biological data.
        Int J Mol Sci. 2017; 18: 412
        • Chakraborty I.
        • Choudhury A.
        Artificial intelligence in biological data.
        J Inform Tech Softw Eng. 2017; 7: 4
      8. Reuters. Gene therapy for blindness appears initially effective, says U.S. FDA. The treatment will be reviewed by an outside panel this week. 2017. Available at: https://www.scientificamerican.com/article/gene-therapy-for-blindness-appears-initially-effective-says-u-s-fda/. Accessd May 8, 2018.

        • Hagen M.E.
        • Jung M.K.
        • Ris F.
        • et al.
        Early clinical experience with the da Vinci Xi surgical system in general surgery.
        J Robot Surg. 2017; 11: 347-353
        • Alterovitz R.
        • Koenig S.
        • Likhachev M.
        Robot planning in the real world: research challenges and opportunities.
        AI Mag. 2016; 37: 76-84
      9. Larsson D, Irving D, Effendi S. Ophthalmic robot. 30th Florida Conference on Recent Advances in Robotics. Boca Raton (FL), May 11-12, 2017.

        • Stark H.
        Prepare yourselves, robots will soon replace doctors in healthcare.
        Forbes Tech. 2017;
        • Anupama J.
        • Thubagere A.J.
        • Wei Li W.
        • et al.
        A cargo-sorting DNA robot.
        Science. 2017; 357 ([pii:eaan6558])
        • Bresnick J.
        Machine learning, artificial intelligence gain healthcare momentum.
        Health Analytics. 2017;
        • Reeves M.
        • Moldoveaunu M.
        Artificial intelligence: the gap between promise and practice.
        Scientific America. 2017;
        • Catania L.
        • McGreal J.
        • Edlow R.
        Modern refraction faces a fiddler on the roof.
        Optometric Management. 2012; 47: 32-36
        • Edwards P.
        The 13 most common questions about HAL, the computer from 2001.
        Trivia Happy. 2014;
      10. Available at: http://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html. Accessed February 17, 2011.

        • Knight W.
        AI’s language problem.
        MIT Technology Review. 2016;
        • Kreps G.L.
        • Neuhauser L.
        Artificial intelligence and immediacy: designing health communication to personally engage consumers and providers.
        Patient Education Couns. 2013; 92: 205-210
        • Knight W.
        The dark secret at the heart of AI.
        MIT Technology Review. 2017;
        • Obermeyer Z.
        • Phil M.
        • Emanuel E.J.
        Predicting the future — big data, machine learning, and clinical medicine.
        N Engl J Med. 2016; 375: 1216-1219