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山东大学学报 (医学版) ›› 2020, Vol. 58 ›› Issue (11): 1-10.doi: 10.6040/j.issn.1671-7554.0.2020.1181

• 眼科人工智能新进展专题 •    下一篇

人工智能在眼科真实临床场景的应用：机遇和挑战

何明光1,*(),刘驰1,2,李治玺1   

  1. 1. 中山大学中山眼科中心，眼科学国家重点实验室, 广东 广州 510060
    2. 悉尼科技大学计算机学院, 澳大利亚 悉尼 2007
  • 收稿日期:2020-08-17 出版日期:2020-11-10 发布日期:2020-11-04
  • 通讯作者: 何明光 E-mail:mingguang_he@yahoo.com
  • 作者简介:何明光，中山大学中山眼科中心二级教授，博士研究生导师，亚太眼科学会副秘书长和理事，亚太远程眼科学会主席，中华医学会眼科分会防盲和流行病学学组组长。国家杰出青年基金获得者、国家“万人计划”领军人才、广东特支计划杰出人才(“南粤百杰”)。研究领域主要包括眼科疾病的临床和遗传流行病学研究、近视防控、双生子研究、图像技术研究、人工智能和大数据研究。以第一或通讯作者在《JAMA》《Lancet》《Diabetes care》《Ophthalmology》等期刊发表原创论文370篇，连续6年被评为中国医学领域高被引学者
  • 基金资助:
    国家重点研发计划(2018YFC0116500);国家自然科学基金眼科国家重点实验室基础研究(81420108008);广东省科技计划项目(2013B20400003)

Applying artificial intelligence in ophthalmic real-world practice: opportunities and challenges

Mingguang HE1,*(),Chi LIU1,2,Zhixi LI1   

  1. 1. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, Guangdong, China
    2. School of Computer Science, University of Technology Sydney, Sydney 2007, NSW, Australia
  • Received:2020-08-17 Online:2020-11-10 Published:2020-11-04
  • Contact: Mingguang HE E-mail:mingguang_he@yahoo.com

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摘要：

人工智能在包括眼科在内的许多临床学科中已经从实验阶段快速发展到实施阶段。以数据驱动的深度学习技术为基于眼科影像数据的自动化诊断技术创造了前所未有的机会，显著提高眼科医疗系统的可及性、效率和成本效益。虽然这项技术在不久的将来，必然会对临床流程和实践模式产生深远影响，但将其转化为真实世界临床实践仍然具有挑战性。本文将在介绍这一领域的一些进展的基础上，重点总结人工智能在眼科真实医疗场景投放中的机遇和挑战，指出了其中可能会存在的一系列潜在问题，例如诊断偏差、医学伦理和法律问题、临床评价和产品注册规范性等，以期促进该技术与临床实践的结合，进一步提升眼科人工智能的应用价值。

关键词: 人工智能, 眼科, 真实世界投放, 临床实践

Abstract:

Artificial intelligence has rapidly evolved from the experimental phase to the implementation phase in many clinical disciplines, including ophthalmology. The data-driven deep learning technology has created unprecedented opportunities for major breakthroughs in the imaging data-based automated diagnoses in ophthalmology, significantly improving the accessibility, efficiency, and cost-effectiveness of eye care systems. Although this technology will have a profound impact on clinical flow and practice patterns sooner or later, translating such a technology into clinical practice is challenging. With comprehensively going through the latest progress in this research domain, this article highlights the opportunities and challenges of the real-world deployment of artificial intelligence in ophthalmology, and figures out the potential problems that may arise during the transition, such as diagnosis bias, clinical evaluation, medical accountability, as well as ethical and legal issues. The discovery could facilitate the integration of artificial intelligence into routine clinical practice and further improve the relevant applications.

Key words: Artificial intelligence, Ophthalmology, Real-world deployment, Clinical practice

中图分类号: 

  • R770.4

图1

AI分类机会筛查模型"

图2

AI用做初始分类策略以最大化人工分级的效率"

图3

3种临床途径整合模式:分类、替换和附加"

1 McCarthy J , Minsky M , Rochester N , et al. A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955[J]. AI Magazine, 2006, 27 (4): 12- 14.
doi: 10.1609/aimag.v27i4.1904
2 LeCun Y , Bengio Y , Hinton G . Deep learning[J]. Nature, 2015, 521: 436- 444.
doi: 10.1038/nature14539
3 Wang J , Ding H , Bidgoli FA , et al. Detecting cardiovascular disease from mammograms with deep learning[J]. IEEE transactions on medical imaging, 2017, 36 (5): 1172- 1181.
doi: 10.1109/TMI.2017.2655486
4 Sarraf S, Tofighi G. Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data[C]//2016 Future Technologies Conference (FTC). December 6-7, 2016, San Francisco, CA, USA. IEEE, 2016: 816-820. doi: 10.1109/FTC.2016.7821697.
5 Zech JR , Badgeley MA , Liu M , et al. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study[J]. PLoS Medicine, 2018, 15 (11): e1002683.
doi: 10.1371/journal.pmed.1002683
6 Coudray N , Ocampo PS , Sakellaropoulos T , et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning[J]. Nature Medicine, 2018, 24 (10): 1559- 1567.
doi: 10.1038/s41591-018-0177-5
7 O'Neill EC , Gurria LU , Pandav SS , et al. Glaucomatous optic neuropathy evaluation project: factors associated with underestimation of glaucoma likelihood[J]. JAMA Ophthalmol, 2014, 132 (5): 560- 566.
doi: 10.1001/jamaophthalmol.2014.96
8 Kong YX , Coote MA , O'Neill EC , et al. Glaucomatous optic neuropathy evaluation project: a standardized internet system for assessing skills in optic disc examination[J]. Clin ExpOphthalmol, 2011, 39 (4): 308- 317.
9 Sundling V , Gulbrandsen P , Straand J . Sensitivity and specificity of Norwegian optometrists' evaluation of diabetic retinopathy in single-field retinal images-a cross-sectional experimental study[J]. BMC Health Services Research, 2013, 13: 17.
doi: 10.1186/1472-6963-13-17
10 Abramoff MD , Lou Y , Erginay A , et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning[J]. Invest Ophthalmol Vis Sci, 2016, 57 (13): 5200- 5206.
doi: 10.1167/iovs.16-19964
11 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[J]. JAMA, 2016, 316 (22): 2402- 2410.
doi: 10.1001/jama.2016.17216
12 Ting D , Cheung CY , Lim G , et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes[J]. JAMA, 2017, 318 (22): 2211- 2223.
doi: 10.1001/jama.2017.18152
13 Tufail A , Rudisill C , Egan C , et al. Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders[J]. Ophthalmol, 2017, 124 (3): 343- 351.
doi: 10.1016/j.ophtha.2016.11.014
14 Abramoff MD , Lavin PT , Birch M , et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices[J]. NPJ Digital Medicine, 2018, 1: 39.
doi: 10.1038/s41746-018-0040-6
15 Li Z , Keel S , Liu C , et al. An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs[J]. Diabetes Care, 2018, 41 (12): 2509- 2516.
doi: 10.2337/dc18-0147
16 Verbraak FD , Abramoff MD , Bausch GCF , et al. Diagnostic accuracy of a device for the automated detection of diabetic retinopathy in a primary care setting[J]. Diabetes Care, 2019, 42 (4): 651- 656.
17 Li Z , He Y , Keel S , et al. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs[J]. Ophthalmol, 2018, 125 (8): 1199- 1206.
doi: 10.1016/j.ophtha.2018.01.023
18 Phene S , Dunn RC , Hammel N , et al. Deep learning and glaucoma specialists: the relative importance of optic disc features to predict glaucoma referral in fundus photographs[J]. Ophthalmol, 2019, 126 (12): 1627- 1639.
doi: 10.1016/j.ophtha.2019.07.024
19 Liu H , Li L , Wormstone IM , et al. Development and validation of a deep learning system to detect glaucomatous optic neuropathy using fundus photographs[J]. JAMA Ophthalmol, 2019, 137 (12): 1353- 1360.
doi: 10.1001/jamaophthalmol.2019.3501
20 Grassmann F , Mengelkamp J , Brandl C , et al. A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography[J]. Ophthalmol, 2018, 125 (9): 1410- 1420.
doi: 10.1016/j.ophtha.2018.02.037
21 Burlina PM , Joshi N , Pacheco KD , et al. Use of deep learning for detailed severity characterization and estimation of 5-year risk among patients with age-related macular degeneration[J]. JAMA Ophthalmol, 2018, 136 (12): 1359- 1366.
doi: 10.1001/jamaophthalmol.2018.4118
22 Keenan TD , Dharssi S , Peng Y , et al. A deep learning approach for automated detection of geographic atrophy from color fundus photographs[J]. Ophthalmol, 2019, 126 (11): 1533- 1540.
doi: 10.1016/j.ophtha.2019.06.005
23 Keel S , Li Z , Scheetz J , et al. Development and validation of a deep-learning algorithm for the detection of neovascular age-related macular degeneration from colour fundus photographs[J]. Clin Experiment Ophthalmol, 2019, 47 (8): 1009- 1018.
doi: 10.1111/ceo.13575
24 Brown JM , Campbell JP , Beers A , et al. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks[J]. JAMA Ophthalmol, 2018, 136 (7): 803- 810.
doi: 10.1001/jamaophthalmol.2018.1934
25 Gupta K , Campbell JP , Taylor S , et al. A quantitative severity scale for retinopathy of prematurity using deep learning to monitor disease regression after treatment[J]. JAMA Ophthalmol, 2019, 137 (9): 1029- 1036.
doi: 10.1001/jamaophthalmol.2019.2442
26 Taylor S , Brown JM , Gupta K , et al. Monitoring disease progression with a quantitative severity scale for retinopathy of prematurity using deep learning[J]. JAMA Ophthalmol, 2019, 137 (9): 1022- 1028.
doi: 10.1001/jamaophthalmol.2019.2433
27 Tan Z , Simkin S , Lai C , et al. Deep learning algorithm for automated diagnosis of retinopathy of prematurity plus disease[J]. Transl Vis Sci Technol, 2019, (6): 23.
doi: 10.1167/tvst.8.6.23
28 De Fauw J , Ledsam JR , Romera-Paredes B , et al. Clinically applicable deep learning for diagnosis and referral in retinal disease[J]. Nat Med, 2018, 24 (9): 1342- 1350.
doi: 10.1038/s41591-018-0107-6
29 Kermany DS , Goldbaum M , Cai W , et al. Identifying medical diagnoses and treatable diseases by image-based deep learning[J]. Cell, 2018, 172 (5): 1122- 1131.
doi: 10.1016/j.cell.2018.02.010
30 Schlegl T , Waldstein SM , Bogunovic H , et al. Fully automated detection and quantification of macular fluid in OCT using deep learning[J]. Ophthalmol, 2018, 125 (4): 549- 558.
doi: 10.1016/j.ophtha.2017.10.031
31 Sun Z , Tang F , Wong R , et al. OCT angiography metrics predict progression of diabetic retinopathy and development of diabetic macular edema: a prospective study[J]. Ophthalmol, 2019, 126 (12): 1675- 1684.
doi: 10.1016/j.ophtha.2019.06.016
32 Medeiros FA , Jammal AA , Thompson AC . From machine to machine: an OCT-trained deep learning algorithm for objective quantification of glaucomatous damage in fundus photographs[J]. Ophthalmol, 2019, 126 (4): 513- 521.
doi: 10.1016/j.ophtha.2018.12.033
33 Christopher M , Bowd C , Belghith A , et al. Deep learning approaches predict glaucomatous visual field damage from OCT optic nerve head en face images and retinal nerve fiber layer thickness maps[J]. Ophthalmol, 2020, 127 (3): 346- 356.
doi: 10.1016/j.ophtha.2019.09.036
34 Sample PA , Chan K , Boden C , et al. Using Unsupervised Learning with Variational Bayesian Mixture of Factor Analysis to Identify Patterns of Glaucomatous Visual Field Defects[J]. Invest Ophthalmol Vis Sci, 2004, 45: 2596- 2605.
doi: 10.1167/iovs.03-0343
35 Asaoka R , Murata H , Iwase A , et al. Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier[J]. Ophthalmol, 2016, 123 (9): 1974- 1980.
doi: 10.1016/j.ophtha.2016.05.029
36 Wang M , Tichelaar J , Pasquale LR , et al. Characterization of central visual field loss in end-stage glaucoma by unsupervised artificial intelligence[J]. JAMA Ophthalmol, 2020, 138 (2): 190- 198.
37 Son J , Shin JY , Kim HD , et al. Development and validation of deep learning models for screening multiple abnormal findings in retinal fundus images[J]. Ophthalmol, 2020, 127 (1): 85- 94.
38 Varadarajan AV , Bavishi P , Ruamviboonsuk P , et al. Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning[J]. Nat Commun, 2020, 11 (1): 130.
doi: 10.1038/s41467-019-13922-8
39 Cheung CY , Tang F , Ting DSW , et al. Artificial intelligence in diabetic eye disease screening[J]. Asia Pacific Ophthalmol, 2019, 24
doi: 10.22608/APO.201976
40 Kapoor R , Whigham BT , Al-Aswad LA . Artificial intelligence and optical coherence tomography imaging[J]. Asia Pacific Ophthalmol, 2019, 8 (2): 187- 194.
41 Li Z , Keel S , Liu C , et al. Can artificial intelligence make screening faster, more accurate, and more accessible?[J]. Asia Pacific Ophthalmol, 2018, 7 (6): 436- 441.
42 Tan Z , Scheetz J , He M . Artificial intelligence in ophthalmology: accuracy, challenges, and clinical application[J]. Asia Pacific Ophthalmol, 2019, 8 (3): 197- 199.
43 Schmidt Erfurth U , Sadeghipour A , Gerendas BS , et al. Artificial intelligence in retina[J]. Prog Retin Eye Res, 2018, 67: 1- 29.
doi: 10.1016/j.preteyeres.2018.07.004
44 Ting DSW , Peng L , Varadarajan AV , et al. Deep learning in ophthalmology: The technical and clinical considerations[J]. Prog Retin Eye Res, 2019, 72: 100759.
doi: 10.1016/j.preteyeres.2019.04.003
45 Bellemo V , Lim ZW , Lim G , et al. Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study[J]. Lancet Digit Heal, 2019, 1 (1): e35- e44.
doi: 10.1016/S2589-7500(19)30004-4
46 Quellec G , Lamard M , Conze PH , et al. Automatic detection of rare pathologies in fundus photographs using few-shot learning[J]. Med Image Anal, 2020, 61: 101660.
doi: 10.1016/j.media.2020.101660
47 Burlina PM , Joshi N , Pekala M , et al. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks[J]. JAMA Ophthalmol, 2017, 135 (11): 1170- 1176.
doi: 10.1001/jamaophthalmol.2017.3782
48 Burlina P , Pacheco KD , Joshi N , et al. Comparing humans and deep learning performance for grading AMD: a study in using universal deep features and transfer learning for automated AMD analysis[J]. Comput Biol Med, 2017, 82: 80- 86.
doi: 10.1016/j.compbiomed.2017.01.018
49 Samagaio G , Estévez A , Moura J , et al. Automatic macular edema identification and characterization using OCT images[J]. Comput Methods Programs Biomed, 2018, 163: 47- 63.
doi: 10.1016/j.cmpb.2018.05.033
50 Sayres R , Taly A , Rahimy E , et al. Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy[J]. Ophthalmol, 2019, 126 (4): 552- 564.
doi: 10.1016/j.ophtha.2018.11.016
51 Chakravarthy U , Goldenberg D , Young G , et al. Automated Identification of Lesion Activity in Neovascular Age-Related Macular Degeneration[J]. Ophthalmol, 2016, 123 (8): 1731- 1736.
doi: 10.1016/j.ophtha.2016.04.005
52 Ran AR , Cheung CY , Wang X , et al. Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: a retrospective training and validation deep-learning analysis[J]. Lancet Digit Heal, 2019, 1 (4)
53 Ronneberger O , Fischer P , Brox T . U-net: convolutional networks for biomedical image segmentation[M]. Cham: Springer International Publishing, 2015: 234- 241.
doi: 10.1007/978-3-319-24574-4_28
54 Zheng R , Liu L , Zhang S , et al. Detection of exudates in fundus photographs with imbalanced learning using conditional generative adversarial network[J]. Biomedical Optics Express, 2018, 9 (10): 4863- 4878.
doi: 10.1364/BOE.9.004863
55 Zhao HL, Sun NL. Improved U-net model for nerve segmentation[M]//Lecture Notes in Computer Science. Cham: Springer International Publishing, 2017: 496-504. doi: 10.1007/978-3-319-71589-6_43.
56 Burewar S, Gonde AB, Vipparthi SK. Diabetic Retinopathy Detection by Retinal segmentation with Region merging using CNN[C]//2018 IEEE 13th International Conference on Industrial and Information Systems (ICⅡS). December 1-2, 2018, Rupnagar, India. IEEE, 2018: 136-142. doi: 10.1109/ICⅡNFS.2018.8721315.
57 Tennakoon R, Gostar AK, Hoseinnezhad R, et al. Retinal fluid segmentation in OCT images using adversarial loss based convolutional neural networks[C]//2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). April 4-7, 2018, Washington, DC, USA. IEEE, 2018: 1436-1440. doi: 10.1109/ISBI.2018.8363842.
58 Keel S , Wu J , Lee PY , Scheetz J , He M . Visualizing Deep Learning Models for the Detection of Referable Diabetic Retinopathy and Glaucoma[J]. JAMA Ophthalmology, 2019, 137 (3): 288- 292.
doi: 10.1001/jamaophthalmol.2018.6035
59 Gargeya R , Leng T . Automated Identification of Diabetic Retinopathy Using Deep Learning[J]. Ophthalmology, 2017, 124 (7): 962- 969.
doi: 10.1016/j.ophtha.2017.02.008
60 Moosavi-Dezfooli SM, Fawzi A, Frossard P. DeepFool: a simple and accurate method to fool deep neural networks[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 27-30, 2016, Las Vegas, NV, USA. IEEE, 2016: 2574-2582. doi: 10.1109/CVPR.2016.282.
61 Su JW , Vargas DV , Sakurai K . One pixel attack for fooling deep neural networks[J]. IEEE Trans Evol Comput, 2019, 23 (5): 828- 841.
doi: 10.1109/TEVC.2019.2890858
62 Rohm M , Tresp V , Muller M , et al. Predicting visual acuity by using machine learning in patients treated for neovascular age-related macular degeneration[J]. Ophthalmol, 2018, 125 (7): 1028- 1036.
doi: 10.1016/j.ophtha.2017.12.034
63 Arcadu F , Benmansour F , Maunz A , et al. Deep learning algorithm predicts diabetic retinopathy progression in individual patients[J]. NPJ Digit Med, 2019, 2: 92.
doi: 10.1038/s41746-019-0172-3
64 Christopher M , Belghith A , Weinreb RN , et al. Retinal nerve fiber layer features identified by unsupervised machine learning on optical coherence tomography scans predict Glaucoma progression[J]. Invest Ophthalmol Vis Sci, 2018, 59 (7): 2748- 2756.
doi: 10.1167/iovs.17-23387
65 Poplin R , Varadarajan AV , Blumer K , et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning[J]. Nat Biomed Eng, 2018, 2 (3): 158- 164.
doi: 10.1038/s41551-018-0195-0
66 Elmore JG , Wells CK , Lee CH , et al. Variability in radiologists' interpretations of mammograms[J]. N Engl J Med, 1994, 331 (22): 1493- 1499.
doi: 10.1056/NEJM199412013312206
67 Elmore JG , Longton GM , Carney PA , et al. Diagnostic concordance among pathologists interpreting breast biopsy specimens[J]. JAMA, 2015, 313 (11): 1122- 1132.
doi: 10.1001/jama.2015.1405
68 Maspero M , Savenije MHF , Dinkla AM , et al. Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy[J]. Phys Med Biol, 2018, 63 (18): 185001.
doi: 10.1088/1361-6560/aada6d
69 Jin CB , Kim H , Liu M , et al. Deep CT to MR synthesis using paired and unpaired data[J]. Sensors (Basel), 2019, 19 (10): 2361.
doi: 10.3390/s19102361
70 Yang QS , Yan PK , Zhang YB , et al. Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss[J]. IEEE Trans Med Imaging, 2018, 37 (6): 1348- 1357.
doi: 10.1109/TMI.2018.2827462
71 Yi X , Walia E , Babyn P . Generative adversarial network in medical imaging: a review[J]. Med Image Anal, 2019, 58: 101552.
doi: 10.1016/j.media.2019.101552
72 Bossuyt PM , Reitsma JB , Bruns DE , et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies[J]. Clin Chem, 2015, 61 (12): 1446- 1452.
doi: 10.1373/clinchem.2015.246280
73 Collins GS , Reitsma JB , Altman DG , et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement[J]. Br J Surg, 2015, 102 (3): 148- 158.
74 CONSORT-AI and SPIRIT-AI Steering Group . Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed[J]. Nat Med, 2019, 25 (10): 1467- 1468.
doi: 10.1038/s41591-019-0603-3
75 Liu XX , Faes L , Calvert MJ , et al. Extension of the CONSORT and SPIRIT statements[J]. Lancet, 2019, 394 (10205): 1225.
doi: 10.1016/S0140-6736(19)31819-7
76 Collins GS , Moons KGM . Reporting of artificial intelligence prediction models[J]. Lancet, 2019, 393 (10181): 1577- 1579.
doi: 10.1016/S0140-6736(19)30037-6
77 Lin HT , Li RY , Liu ZZ , et al. Diagnostic efficacy and therapeutic decision-making capacity of an artificial intelligence platform for childhood cataracts in eye clinics: a multicentre randomized controlled trial[J]. EClinicalMedicine, 2019, 9: 52- 59.
doi: 10.1016/j.eclinm.2019.03.001
78 Bossuyt PM , Irwig L , Craig J , et al. Comparative accuracy: assessing new tests against existing diagnostic pathways[J]. BMJ, 2006, 332 (7549): 1089- 1092.
doi: 10.1136/bmj.332.7549.1089
79 Akhtar N , Mian A . Threat of adversarial attacks on deep learning in computer vision: a survey[J]. IEEE Access, 2018, 6: 14410- 14430.
doi: 10.1109/ACCESS.2018.2807385
80 Finlayson SG , Bowers JD , Ito J , et al. Adversarial attacks on medical machine learning[J]. Science, 2019, 363 (6433): 1287- 1289.
doi: 10.1126/science.aaw4399
81 FDA. Software as a Medical Device[ER/OL]. (2018-12-04).[2020-03-08]. https://www.fda.gov/medical-devices/digital-health/software-medical-device-samd.
82 FDA. Digital Health Innovation Action Plan[ER/OL]. (2018).[2020-03-08]. https://www.fda.gov/media/106331/download.
83 FDA. FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems[ER/OL]. (2018-12-04).[2020-05-13]. https://www.fda.gov/news-events/press-announcements/fda-permits-marketing-artificial-intelligence-bas-ed-device-detect-certain-diabetes-related-eye.
84 FDA. FDA permits marketing of clinical decision support software for alerting providers of a potential stroke in patients[ER/OL]. (2018-02-13).[2020-05-13]. https://www.fda.gov/news-events/press-announcements/fda-permits-marketing-artificial-intelligence-based-device-detect-certain-diabetes-related-eye.
85 Harvard Business Review. What artificial intelligence can and can't do right now[ER/OL]. (2016-11-09).[2020-10-10]. https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now.
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