Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, Peng L, Webster DR. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. 2018;2(3):158–64.Crossref
Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, Peng L, Webster DR. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. 2018;2(3):158–64.Crossref
Chen J, See KC. Artificial intelligence for COVID-19: rapid review. J Med Internet Res. 2020;22(10):e21476.Crossref
Chen J, See KC. Artificial intelligence for COVID-19: rapid review. J Med Internet Res. 2020;22(10):e21476.Crossref
Lee AY, Yanagihara RT, Lee CS, Blazes M, Jung HC, Chee YE, Gencarella MD, Gee H, Maa AY, Cockerham GC, Lynch M, Boyko EJ. Multicenter, head-to-head, real-world validation study of seven automated artificial intelligence diabetic retinopathy screening systems. Diabetes Care. 2021;dc201877. https://doi.org/10.2337/dc20-1877.
Lee AY, Yanagihara RT, Lee CS, Blazes M, Jung HC, Chee YE, Gencarella MD, Gee H, Maa AY, Cockerham GC, Lynch M, Boyko EJ. Multicenter, head-to-head, real-world validation study of seven automated artificial intelligence diabetic retinopathy screening systems. Diabetes Care. 2021;dc201877. https://doi.org/10.2337/dc20-1877.
Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015-20): a comparative analysis. Lancet Digit Health. 2021;3(3):e195–203.[Crossref](https://doi.org/10.1016/S2589-7500(20)30292-2)
Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015-20): a comparative analysis. Lancet Digit Health. 2021;3(3):e195–203.[Crossref](https://doi.org/10.1016/S2589-7500(20)30292-2)
Hwang TJ, Kesselheim AS, Vokinger KN. Lifecycle regulation of artificial intelligence- and machine learning-based software devices in medicine. JAMA. 2019;322(23):2285–6.Crossref
Hwang TJ, Kesselheim AS, Vokinger KN. Lifecycle regulation of artificial intelligence- and machine learning-based software devices in medicine. JAMA. 2019;322(23):2285–6.Crossref
Hwang TJ, Sokolov E, Franklin JM, Kesselheim AS. Comparison of rates of safety issues and reporting of trial outcomes for medical devices approved in the European Union and United States: cohort study. BMJ. 2016;353:i3323.Crossref
Hwang TJ, Sokolov E, Franklin JM, Kesselheim AS. Comparison of rates of safety issues and reporting of trial outcomes for medical devices approved in the European Union and United States: cohort study. BMJ. 2016;353:i3323.Crossref
European Commission. Medical devices—EUDAMED. 17 June 2020. https://ec.europa.eu/growth/sectors/medical-devices/new-regulations/eudamed_en. Accessed 15 Jan 2021.
European Commission. Medical devices—EUDAMED. 17 June 2020. https://ec.europa.eu/growth/sectors/medical-devices/new-regulations/eudamed_en. Accessed 15 Jan 2021.
Ting DSW, Liu Y, Burlina P, Xu X, Bressler NM, Wong TY. AI for medical imaging goes deep. Nat Med. 2018;24(5):539–40.Crossref
Ting DSW, Liu Y, Burlina P, Xu X, Bressler NM, Wong TY. AI for medical imaging goes deep. Nat Med. 2018;24(5):539–40.Crossref
Wang SY, Pershing S, Lee AY, AAO Taskforce on AI and AAO Medical Information Technology Committee. Big data requirements for artificial intelligence. Curr Opin Ophthalmol. 2020;31(5):318–23.Crossref
Wang SY, Pershing S, Lee AY, AAO Taskforce on AI and AAO Medical Information Technology Committee. Big data requirements for artificial intelligence. Curr Opin Ophthalmol. 2020;31(5):318–23.Crossref
Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F, Dong J, Prasadha MK, Pei J, Ting MYL, Zhu J, Li C, Hewett S, Dong J, Ziyar I, Shi A, Zhang R, Zheng L, Hou R, Shi W, Fu X, Duan Y, Huu VAN, Wen C, Zhang ED, Zhang CL, Li O, Wang X, Singer MA, Sun X, Xu J, Tafreshi A, Lewis MA, Xia H, Zhang K. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172(5):1122–1131.e9.Crossref
Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F, Dong J, Prasadha MK, Pei J, Ting MYL, Zhu J, Li C, Hewett S, Dong J, Ziyar I, Shi A, Zhang R, Zheng L, Hou R, Shi W, Fu X, Duan Y, Huu VAN, Wen C, Zhang ED, Zhang CL, Li O, Wang X, Singer MA, Sun X, Xu J, Tafreshi A, Lewis MA, Xia H, Zhang K. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172(5):1122–1131.e9.Crossref
Rampasek L, Goldenberg A. Learning from everyday images enables expert-like diagnosis of retinal diseases. Cell. 2018;172(5):893–5.Crossref
Rampasek L, Goldenberg A. Learning from everyday images enables expert-like diagnosis of retinal diseases. Cell. 2018;172(5):893–5.Crossref
Burlina P, Paul W, Mathew P, Joshi N, Pacheco KD, Bressler NM. Low-shot deep learning of diabetic retinopathy with potential applications to address artificial intelligence bias in retinal diagnostics and rare ophthalmic diseases. JAMA Ophthalmol. 2020;138(10):1070–7.Crossref
Burlina P, Paul W, Mathew P, Joshi N, Pacheco KD, Bressler NM. Low-shot deep learning of diabetic retinopathy with potential applications to address artificial intelligence bias in retinal diagnostics and rare ophthalmic diseases. JAMA Ophthalmol. 2020;138(10):1070–7.Crossref
Burlina PM, Joshi N, Pacheco KD, Liu TYA, Bressler NM. Assessment of deep generative models for high-resolution synthetic retinal image generation of age-related macular degeneration. JAMA Ophthalmol. 2019;137:258–64.Crossref
Burlina PM, Joshi N, Pacheco KD, Liu TYA, Bressler NM. Assessment of deep generative models for high-resolution synthetic retinal image generation of age-related macular degeneration. JAMA Ophthalmol. 2019;137:258–64.Crossref
Liu Y, Yang J, Zhou Y, Wang W, Zhao J, Yu W, Zhang D, Ding D, Li X, Chen Y. Prediction of OCT images of short-term response to anti-VEGF treatment for neovascular age-related macular degeneration using generative adversarial network. Br J Ophthalmol. 2020;104(12):1735–40.Crossref
Liu Y, Yang J, Zhou Y, Wang W, Zhao J, Yu W, Zhang D, Ding D, Li X, Chen Y. Prediction of OCT images of short-term response to anti-VEGF treatment for neovascular age-related macular degeneration using generative adversarial network. Br J Ophthalmol. 2020;104(12):1735–40.Crossref
Zheng C, Xie X, Zhou K, Chen B, Chen J, Ye H, Li W, Qiao T, Gao S, Yang J, Liu J. Assessment of generative adversarial networks model for synthetic optical coherence tomography images of retinal disorders. Transl Vis Sci Technol. 2020;9(2):29.Crossref
Zheng C, Xie X, Zhou K, Chen B, Chen J, Ye H, Li W, Qiao T, Gao S, Yang J, Liu J. Assessment of generative adversarial networks model for synthetic optical coherence tomography images of retinal disorders. Transl Vis Sci Technol. 2020;9(2):29.Crossref
Liu TYA, Farsiu S, Ting DS. Generative adversarial networks to predict treatment response for neovascular age-related macular degeneration: interesting, but is it useful? Br J Ophthalmol. 2020;104(12):1629–30.Crossref
Liu TYA, Farsiu S, Ting DS. Generative adversarial networks to predict treatment response for neovascular age-related macular degeneration: interesting, but is it useful? Br J Ophthalmol. 2020;104(12):1629–30.Crossref
Lee CS, Lee AY. Clinical applications of continual learning machine learning. Lancet Digit Health. 2020;2(6):e279–81.[Crossref](https://doi.org/10.1016/S2589-7500(20)30102-3)
Lee CS, Lee AY. Clinical applications of continual learning machine learning. Lancet Digit Health. 2020;2(6):e279–81.[Crossref](https://doi.org/10.1016/S2589-7500(20)30102-3)
Mehta N, Lee CS, Mendonça LSM, Raza K, Braun PX, Duker JS, Waheed NK, Lee AY. Model-to-data approach for deep learning in optical coherence tomography intraretinal fluid segmentation. JAMA Ophthalmol. 2020;138(10):1017–24.Crossref
Mehta N, Lee CS, Mendonça LSM, Raza K, Braun PX, Duker JS, Waheed NK, Lee AY. Model-to-data approach for deep learning in optical coherence tomography intraretinal fluid segmentation. JAMA Ophthalmol. 2020;138(10):1017–24.Crossref
Larson DB, Harvey H, Rubin DL, Irani N, Tse JR, Langlotz CP. Regulatory frameworks for development and evaluation of artificial intelligence-based diagnostic imaging algorithms: summary and recommendations. J Am Coll Radiol. 2021;18(3 Pt A):413–24.Crossref
Larson DB, Harvey H, Rubin DL, Irani N, Tse JR, Langlotz CP. Regulatory frameworks for development and evaluation of artificial intelligence-based diagnostic imaging algorithms: summary and recommendations. J Am Coll Radiol. 2021;18(3 Pt A):413–24.Crossref
Wang X, Liang G, Zhang Y, Blanton H, Bessinger Z, Jacobs N. Inconsistent performance of deep learning models on mammogram classification. J Am Coll Radiol. 2020;17:796–803.Crossref
Wang X, Liang G, Zhang Y, Blanton H, Bessinger Z, Jacobs N. Inconsistent performance of deep learning models on mammogram classification. J Am Coll Radiol. 2020;17:796–803.Crossref
Subbaswamy A, Schulam P, Saria S. Preventing failures due to dataset shift: Learning predictive models that transport. Proc Mach Learn Res. 2019;89:3118–27.
Subbaswamy A, Schulam P, Saria S. Preventing failures due to dataset shift: Learning predictive models that transport. Proc Mach Learn Res. 2019;89:3118–27.
Subbaswamy A, Saria S. From development to deployment: dataset shift, causality, and shift-stable models in health AI. Biostatistics. 2020;21:345–52.PubMed
Subbaswamy A, Saria S. From development to deployment: dataset shift, causality, and shift-stable models in health AI. Biostatistics. 2020;21:345–52.PubMed
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17:195.Crossref
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17:195.Crossref
Winkler JK, Fink C, Toberer F. Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition. JAMA Dermatol. 2019;155:1135–41.Crossref
Winkler JK, Fink C, Toberer F. Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition. JAMA Dermatol. 2019;155:1135–41.Crossref
Finlayson SG, Bowers JD, Ito J. Adversarial attacks on medical machine learning. Science. 2019;363:1287–9.Crossref
Finlayson SG, Bowers JD, Ito J. Adversarial attacks on medical machine learning. Science. 2019;363:1287–9.Crossref
Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med. 2018;15:e1002683.Crossref
Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med. 2018;15:e1002683.Crossref
Antun V, Renna F, Poon C, Adcock B, Hansen AC. On instabilities of deep learning in image reconstruction and the potential costs of AI. Proc Natl Acad Sci U S A. 2020; pii: 201907377. https://doi.org/10.1073/pnas.1907377117.
Antun V, Renna F, Poon C, Adcock B, Hansen AC. On instabilities of deep learning in image reconstruction and the potential costs of AI. Proc Natl Acad Sci U S A. 2020; pii: 201907377. https://doi.org/10.1073/pnas.1907377117.
Matheny ME, Thadaney Israni S, Ahmed M, Whicher D. AI in health care: the hope, the hype, the promise, the peril. Washington, DC: National Academy of Medicine; 2019. https://nam.edu/artificial-intelligence-special-publication
Matheny ME, Thadaney Israni S, Ahmed M, Whicher D. AI in health care: the hope, the hype, the promise, the peril. Washington, DC: National Academy of Medicine; 2019. https://nam.edu/artificial-intelligence-special-publication
Norgeot B, Quer G, Beaulieu-Jones BK, Torkamani A, Dias R, Gianfrancesco M, Arnaout R, Kohane IS, Saria S, Topol E, Obermeyer Z, Yu B, Butte AJ. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nat Med. 2020;26(9):1320–4.Crossref
Norgeot B, Quer G, Beaulieu-Jones BK, Torkamani A, Dias R, Gianfrancesco M, Arnaout R, Kohane IS, Saria S, Topol E, Obermeyer Z, Yu B, Butte AJ. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nat Med. 2020;26(9):1320–4.Crossref
Floridi L, Cowls J, King TC, Taddeo M. How to design AI for social good: seven essential factors. Sci Eng Ethics. 2020;26(3):1771–96.Crossref
Floridi L, Cowls J, King TC, Taddeo M. How to design AI for social good: seven essential factors. Sci Eng Ethics. 2020;26(3):1771–96.Crossref
Xie Y, Nguyen QD, Hamzah H, Lim G, Bellemo V, Gunasekeran DV, Yip MYT, Qi Lee X, Hsu W, Li Lee M, Tan CS, Tym Wong H, Lamoureux EL, Tan GSW, Wong TY, Finkelstein EA, Ting DSW. Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study. Lancet Digit Health. 2020;2(5):e240–9.[Crossref](https://doi.org/10.1016/S2589-7500(20)30060-1)
Xie Y, Nguyen QD, Hamzah H, Lim G, Bellemo V, Gunasekeran DV, Yip MYT, Qi Lee X, Hsu W, Li Lee M, Tan CS, Tym Wong H, Lamoureux EL, Tan GSW, Wong TY, Finkelstein EA, Ting DSW. Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study. Lancet Digit Health. 2020;2(5):e240–9.[Crossref](https://doi.org/10.1016/S2589-7500(20)30060-1)
Tufail A, Rudisill C, Egan C, Kapetanakis VV, Salas-Vega S, Owen CG, Lee A, Louw V, Anderson J, Liew G, Bolter L, Srinivas S, Nittala M, Sadda S, Taylor P, Rudnicka AR. Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders. Ophthalmology. 2017;124(3):343–51.Crossref
Tufail A, Rudisill C, Egan C, Kapetanakis VV, Salas-Vega S, Owen CG, Lee A, Louw V, Anderson J, Liew G, Bolter L, Srinivas S, Nittala M, Sadda S, Taylor P, Rudnicka AR. Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders. Ophthalmology. 2017;124(3):343–51.Crossref
© Springer Nature Switzerland AG 2021
© Springer Nature Switzerland AG 2021
A. Grzybowski (ed.) Искусственный интеллект в офтальмологии<https://doi.org/10.1007/978-3-030-78601-4_2>
A. Grzybowski (ed.)Artificial Intelligence in Ophthalmology<https://doi.org/10.1007/978-3-030-78601-4_2>
Дальше — с подпиской Awamed+
Осталось ещё 1 страница источника. Подписка открывает материал целиком вместе с оригиналом, рисунками и файлами.