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    Real-World Translation of Artificial Intelligence in Neuro-Ophthalmology: The Challenges of Making an Artificial Intelligence System Applicable to Clinical Practice

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    1. Lin D, Xiong J, Liu C, Zhao L, Li Z, Yu S, Wu X, Ge Z, Hu X, Wang B, Fu M, Zhao X, Wang X, Zhu Y, Chen C, Li T, Li Y, Wei W, Zhao M, Li J, Xu F, Ding L, Tan G, Xiang Y, Hu Y, Zhang P, Han Y, li J, Wei L, Zhu P, Liu Y, Chen W, Ting D, Wong T, Chen Y, Lin H. Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study. Lancet Digit Health. 2021;3:e486-e495. 2. Xie Y, Nguyen Q, Bellemo V, Yip M, Lee M, Hamzah H, Lim G, Hsu W, Lee ML, Wang JJ, Cheng CY, Finkelstein EA, Lamoureux EL, Tan GSW, Wong T. Cost-Effectiveness analysis of an artificial intelligence-assisted deep learning system implemented in the national tele-medicine diabetic retinopathy screening in Singapore. Invest Ophthalmol Vis Sci. 2019;60:5471. 3. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus Photographs. JAMA. 2016;316:2402-2410. 4. van der Heijden AA, Abramoff MD, Verbraak F, van Hecke M, Liem A, Nijpels G. Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System. Acta Ophthalmol. 2018;96:63-68. 5. Milea D, Najjar RP, Jiang Z, Ting D, Vasseneix C, Xu X, Aghsaei Fard M, Fonseca P, Vanikieti K, Lagrèze WA, La Morgia C, Cheung CY, Hamann S, Chiquet C, Sanda N, Yang H, Mejico LJ, Rougier MB, Kho R, Tran THC, Singhal S, Gohier P, Vignal-Clermont C, Cheng Cy, Jonas JB, Yu-Wai-Man P, Fraser CL, Chen JJ, Ambika S, Miller NR, Liu Y, Newman NJ, Wong TY, Biousse V. Artificial intelligence to detect papilledema from ocular fundus Photographs. New Engl J Med. 2020;382:1687-1695
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