[ 1 ] Bejnordi BE, Veta M, van Diest PJ, van Ginneken B, Karssemeijer N, Litjens G, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 2017;318 (22):2199–210. link1

[ 2 ] Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologistlevel classification of skin cancer with deep neural networks. Nature 2017;542 (7639):115–8. link1

[ 3 ] Sommer A, Taylor HR, Ravilla TD, West S, Lietman TM, Keenan JD, et al. Challenges of ophthalmic care in the developing world. JAMA Ophthalmol 2014;132(5):640–4. link1

[ 4 ] Pascolini D, Mariotti SP. Global estimates of visual impairment: 2010. Br J Ophthalmol 2012;96(5):614–8. link1

[ 5 ] Clemens LE, Jaynes JM, Lim E, Kolar SS, Reins RY, Baidouri H, et al. Designed host defense peptides for the treatment of bacterial keratitis. Investig Ophthalmol Vis Sci 2017;58(14):6273–81. link1

[ 6 ] Gopinathan U, Garg P, Fernandes M, Sharma S, Athmanathan S, Rao GN. The epidemiological features and laboratory results of fungal keratitis: a 10-year review at a referral eye care center in South India. Cornea 2002;21(6):555–9. link1

[ 7 ] Yang Y, Luyten W, Liu L, Moens MF, Tang J, Li J. Forecasting potential diabetes complications. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence; 2014 Jul 27–31; Quebec City, QC, Canada; 2014. p. 313–9. link1

[ 8 ] He T, Guo J, Chen N, Xu X, Wang Z, Fu K, et al. MediMLP: using Grad-CAM to extract principal variables for lung cancer postoperative complication prediction. IEEE J Biomed Health Inf 2020;24(6):1762–71. link1

[ 9 ] Nee P, Li Y, Zhu J, Peng J, Dai Z, Li G, et al. Disease diagnosis prediction of EMR based on BiGRU–Att–CapsNetwork model. In: Proceedings of 2019 IEEE International Conference on Big Data (Big Data); 2019 Feb 27–Mar 2; Los Angeles, CA, USA; 2019. p. 6166–8. link1

[10] Wright AP, Wright AT, McCoy AB, Sittig DF. The use of sequential patternmining to predict next prescribed medications. J Biomed Inf 2015;53:73–80. link1

[11] Scott IM, Cootes TF, Taylor CJ. Improving appearance model matching using local image structure. In: Proceedings of Biennial International Conference on Information Processing in Medical Imaging; 2003 Jul 20–25; Ambleside, UK; 2003. p. 258–69. link1

[12] Manousakas IN, Undrill PE, Cameron GG, Redpath TW. Split-and-merge segmentation of magnetic resonance medical images: performance evaluation and extension to three dimensions. Comput Biomed Res 1998;31 (6):393–412. link1

[13] Zhao Y, Gui W, Chen Z, Tang J, Li L. Medical images edge detection based on mathematical morphology. In: Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference; 2006 Jan 17–18; Shanghai, China; 2006. p. 6492–5. link1

[14] Kaus MR, von Berg J, Weese J, Niessen W, Pekar V. Automated segmentation of the left ventricle in cardiac MRI. Med Image Anal 2004;8(3):245–54. link1

[15] Cordes D, Haughton V, Carew JD, Arfanakis K, Maravilla K. Hierarchical clustering to measure connectivity in fMRI resting-state data. Magn Reson Imaging 2002;20(4):305–17. link1

[16] Pohl KM, Fisher J, Shenton M, McCarley RW, Grimson WEL, Kikinis R, et al. Logarithm odds maps for shape representation. In: Proceedings of International Conference on Medical Image Computing and Computerassisted Intervention; 2006 Oct 1–6; Copenhagen, Denmark; 2006. p. 955–63. link1

[17] Lee LK, Liew SC, Thong WJ. A review of image segmentation methodologies in medical image. In: Sulaiman HA, Othman MA, Othman MFI, Rahim YA, Pee NC， editors. Advanced computer and communication engineering technology. Cham: Springer; 2011. p. 1069–80. link1

[18] Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng 2017;19:221–48. link1

[19] Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017;42 (9):60–88. link1

[20] Zhou X, Takayama R, Wang S, Hara T, Fujita H. Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method. Med Phys 2017;44(10):5221–33. link1

[21] Dunnmon JA, Yi D, Langlotz CP, Ré C, Rubin DL, Lungren MP. Assessment of convolutional neural networks for automated classification of chest radiographs. Radiology 2019;290(2):537–44. link1

[22] Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med 2019;25(6):954–61. link1

[23] Wu N, Phang J, Park J, Shen Y, Huang Z, Zorin M, et al. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans Med Imaging 2020;39(4):1184–94. link1

[24] Chilamkurthy S, Ghosh R, Tanamala S, Biviji M, Campeau NG, Venugopal V, et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet 2018;392(10162):2388–96. link1

[25] Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H. GANbased synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 2018;321:321–31. link1

[26] Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewing DJ, Satam GP, et al. Screening for cardiac contractile dysfunction using an artificial intelligenceenabled electrocardiogram. Nat Med 2019;25(1):70–4. link1

[27] Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med 2019;25(1):65–9. link1

[28] Bejnordi BE, Veta M, Van Diest PJ, van Ginneken B, Karssemeijer N, Litjen G. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 2017;318(22):2199–210. link1

[29] Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016;316 (22):2402–10. link1

[30] Ting DS, Cheung CY, Lim G, Tan GS, Quang ND, Gan A, 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. JAMA 2017;318(22):2211–23. link1

[31] Kim SJ, Cho KJ, Oh S. Development of machine learning models for diagnosis of glaucoma. PLoS ONE 2017;12(5):e177726. link1

[32] Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 2018;172(5):1122–31. link1

[33] Pan SJ, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng 2009;22(10):1345–59. link1

[34] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014. arXiv:1409.1556.

[35] Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016 Jun 26–Jul 1; Las Vegas, NV, USA; 2016. p. 2818–26. link1

[36] Huang G, Liu Z, van der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2017 Jul 21–26; Honoululu, HI, USA; 2017. p. 4700–8. link1

[37] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997;9 (8):1735–80. link1

[38] Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, et al. Learning phrase representations using RNN encoder–decoder for statistical machine translation. 2014. arXiv:1406.1078.

[39] Sutskever I, Vinyals O, Le QV. Sequence to sequence learning with neural networks. 2014. arXiv:1409.3215.

[40] Schmidhuber J, Wierstra D, Gagliolo M, Gomez F. Training recurrent networks by evolino. Neural Comput 2007;19(3):757–79. link1

[41] Greff K, Srivastava RK, Koutník J, Steunebrink BR, Schmidhuber J. LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Sys 2016;28 (10):2222–32. link1

[42] Wang D, Khosla A, Gargeya R, Irshad H, Beck AH. Deep learning for identifying metastatic breast cancer. 2016. arXiv:1606.05718.

[43] Lam C, Yu C, Huang L, Rubin D. Retinal lesion detection with deep learning using image patches. Invest Ophthalmol Vis Sci 2018;59(1):590–6. link1

[44] Ledley RS, Lusted LB. Reasoning foundations of medical diagnosis. Science 1959;130(3366):9–21. link1

[45] Claus-Christian C. Understanding human perception by human-made illusions. Front Hum Neurosci 2014;8:566. link1

[46] Pan Y. On visual knowledge. Front Inf Technol Electron Eng 2019;20 (8):1021–5. link1

[47] Lecun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521(7553):436–44. link1

[48] Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science 2006;313(5786):504–7. link1

[49] Zhuang YT, Wu F, Chen C, Pan YH. Challenges and opportunities: from big data to knowledge in AI 2.0. Front Inf Technol Electron Eng 2017;18 (1):3–14. link1

[50] Zhu Y, Gao T, Fan L, Huang S, Edmonds M, Liu H, et al. Dark, beyond deep: a paradigm shift to cognitive AI with humanlike common sense. Engineering 2020;6(3):310–45. link1

[51] Begoli E, Bhattacharya T, Kusnezov D. The need for uncertainty quantification in machine-assisted medical decision making. Nat Mach Intell 2019;1 (1):20–3. link1