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Current Status and Future Direction of Artificial Intelligence in Healthcare and Medical Education

의료분야에서 인공지능 현황 및 의학교육의 방향

  • Jung, Jin Sup (Department of Physiology, Pusan National University College of Medicine)
  • 정진섭 (부산대학교 의과대학 생리학교실)
  • Received : 2020.01.31
  • Accepted : 2020.04.09
  • Published : 2020.06.30

Abstract

The rapid development of artificial intelligence (AI), including deep learning, has led to the development of technologies that may assist in the diagnosis and treatment of diseases, prediction of disease risk and prognosis, health index monitoring, drug development, and healthcare management and administration. However, in order for AI technology to improve the quality of medical care, technical problems and the efficacy of algorithms should be evaluated in real clinical environments rather than the environment in which algorithms are developed. Further consideration should be given to whether these models can improve the quality of medical care and clinical outcomes of patients. In addition, the development of regulatory systems to secure the safety of AI medical technology, the ethical and legal issues related to the proliferation of AI technology, and the impacts on the relationship with patients also need to be addressed. Systematic training of healthcare personnel is needed to enable adaption to the rapid changes in the healthcare environment. An overall review and revision of undergraduate medical curriculum is required to enable extraction of significant information from rapidly expanding medical information, data science literacy, empathy/compassion for patients, and communication among various healthcare providers. Specialized postgraduate AI education programs for each medical specialty are needed to develop proper utilization of AI models in clinical practice.

Keywords

References

  1. Lexico. Meaning of artificial intelligence in English [Internet]. Oxford: Lexico, Oxford University Press; c2020 [cited 2020 Jan 9]. Available from: https://www.lexico.com/definition/artificial_intelligence.
  2. Piccinini G. The first computational theory of mind and brain: a close look at Mcculloch and Pitts's "logical calculus of ideas immanent in nervous activity". Synthese. 2004;141(2):175-215. https://doi.org/10.1023/B:SYNT.0000043018.52445.3e
  3. McCarthy J, Minsky ML, Rochester N, Shannon CE. A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Mag. 2006;27(4):12.
  4. Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev. 1958;65(6):386-408. https://doi.org/10.1037/h0042519
  5. Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature. 1986;323(6088):533-6. https://doi.org/10.1038/323533a0
  6. Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18(7):1527-54. https://doi.org/10.1162/neco.2006.18.7.1527
  7. Le Cun Y, Jackel LD, Boser B, Denker JS, Graf HP, Guyon I, et al. Handwritten digit recognition: applications of neural net chips and automatic learning. In: Sanchez-Sinencio E, Lau C, editors, Artificial neural networks. Piscataway (NJ): IEEE Press; 1992. p. 463-8.
  8. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;25:1097-105.
  9. Le QV, Ranzato MA, Monga R, Devin M, Chen K, Corrado GS, et al. Building high-level features using large scale unsupervised learning [Internet]. Ithaca (NY): arXiv, Cornell University; 2012 [cited 2020 Jan 9]. Available from: https://arxiv.org/abs/1112.6209.
  10. Hinton G, Deng L, Yu D, Dahl GE, Mohamed AR, Jaitly N, et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag. 2012;29(6):82-97. https://doi.org/10.1109/MSP.2012.2205597
  11. Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, et al. Mastering the game of Go with deep neural networks and tree search. Nature. 2016;529(7587):484-9. https://doi.org/10.1038/nature16961
  12. 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. https://doi.org/10.1001/jama.2016.17216
  13. Singh H, Meyer AN, Thomas EJ. The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ Qual Saf. 2014;23(9):727-31. https://doi.org/10.1136/bmjqs-2013-002627
  14. Balogh EP, Miller BT, Ball JR; Committee on Diagnostic Error in Health Care; Board on Health Care Services; Institute of Medicine, et al. Improving diagnosis in health care. Washington (DC): National Academies Press; 2015.
  15. Dewa CS, Loong D, Bonato S, Trojanowski L. The relationship between physician burnout and quality of healthcare in terms of safety and acceptability: a systematic review. BMJ Open. 2017;7(6):e015141. https://doi.org/10.1136/bmjopen-2016-015141
  16. Budd K; Association of American Medical Colleges. Will artificial intelligence replace doctors? [Internet]. Washington (DC): Association of American Medical Colleges; 2019 [cited 2020 Jan 9]. Available from: https://www.aamc.org/news-insights/will-artificial-intelligence-replacedoctors.
  17. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. https://doi.org/10.1038/s41591-018-0300-7
  18. Coiera E. The fate of medicine in the time of AI. Lancet. 2018;392(10162):2331-2. https://doi.org/10.1016/S0140-6736(18)31925-1
  19. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94-8. https://doi.org/10.7861/futurehosp.6-2-94
  20. Gong B, Nugent JP, Guest W, Parker W, Chang PJ, Khosa F, et al. Influence of artificial intelligence on canadian medical students' preference for radiology specialty: a national survey study. Acad Radiol. 2019;26(4):566-77. https://doi.org/10.1016/j.acra.2018.10.007
  21. Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Pianykh OS, et al. Current applications and future impact of machine learning in radiology. Radiology. 2018;288(2):318-28. https://doi.org/10.1148/radiol.2018171820
  22. Yala A, Lehman C, Schuster T, Portnoi T, Barzilay R. A deep learning mammography-based model for improved breast cancer risk prediction. Radiology. 2019;292(1):60-6. https://doi.org/10.1148/radiol.2019182716
  23. Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology: new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. 2019;16(11):703-15. https://doi.org/10.1038/s41571-019-0252-y
  24. Chen PC, Gadepalli K, MacDonald R, Liu Y, Kadowaki S, Nagpal K, et al. An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis. Nat Med. 2019;25(9):1453-7. https://doi.org/10.1038/s41591-019-0539-7
  25. Luo H, Xu G, Li C, He L, Luo L, Wang Z, et al. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. Lancet Oncol. 2019;20(12):1645-54. https://doi.org/10.1016/s1470-2045(19)30637-0
  26. Mori Y, Kudo SE, Misawa M, Saito Y, Ikematsu H, Hotta K, et al. Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy: a prospective study. Ann Intern Med. 2018;169(6):357-66. https://doi.org/10.7326/m18-0249
  27. Le Berre C, Sandborn WJ, Aridhi S, Devignes MD, Fournier L, Smail-Tabbone M, et al. Application of artificial intelligence to gastroenterology and hepatology. Gastroenterology. 2020;158(1):76-94. https://doi.org/10.1053/j.gastro.2019.08.058
  28. Thomsen K, Iversen L, Titlestad TL, Winther O. Systematic review of machine learning for diagnosis and prognosis in dermatology. J Dermatolog Treat. 2019:1-15.
  29. Dick V, Sinz C, Mittlbock M, Kittler H, Tschandl P. Accuracy of computer-aided diagnosis of melanoma: a meta-analysis. JAMA Dermatol. 2019;155(11):1291-9. https://doi.org/10.1001/jamadermatol.2019.1375
  30. Cuocolo R, Perillo T, De Rosa E, Ugga L, Petretta M. Current applications of big data and machine learning in cardiology. J Geriatr Cardiol. 2019;16(8):601-7.
  31. Ting DS, Peng L, Varadarajan AV, Keane PA, Burlina PM, Chiang MF, et al. Deep learning in ophthalmology: the technical and clinical considerations. Prog Retin Eye Res. 2019;72:100759. https://doi.org/10.1016/j.preteyeres.2019.04.003
  32. De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;24(9):1342-50. https://doi.org/10.1038/s41591-018-0107-6
  33. Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit Health. 2019;1(6):e271-97. https://doi.org/10.1016/S2589-7500(19)30123-2
  34. 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. https://doi.org/10.1038/s41591-019-0447-x
  35. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94. https://doi.org/10.1038/s41586-019-1799-6
  36. Liu Y, Jain A, Eng C, Way DH, Lee K, Bui P, et al. A deep learning system for differential diagnosis of skin diseases. Nat Med. 2020 May 18 [Epub]. https://doi.org/10.1038/s41591-020-0842-3.
  37. Majkowska A, Mittal S, Steiner DF, Reicher JJ, McKinney SM, Duggan GE, et al. Chest radiograph interpretation with deep learning models: assessment with radiologist-adjudicated reference standards and population-adjusted evaluation. Radiology. 2020;294(2):421-31. https://doi.org/10.1148/radiol.2019191293
  38. Hwang EJ, Nam JG, Lim WH, Park SJ, Jeong YS, Kang JH, et al. Deep learning for chest radiograph diagnosis in the emergency department. Radiology. 2019;293(3):573-80. https://doi.org/10.1148/radiol.2019191225
  39. Hsieh TC, Mensah MA, Pantel JT, Aguilar D, Bar O, Bayat A, et al. PEDIA: prioritization of exome data by image analysis. Genet Med. 2019;21(12):2807-14. https://doi.org/10.1038/s41436-019-0566-2
  40. Bhagwat N, Viviano JD, Voineskos AN, Chakravarty MM; Alzheimer's Disease Neuroimaging Initiative. Modeling and prediction of clinical symptom trajectories in Alzheimer's disease using longitudinal data. PLoS Comput Biol. 2018;14(9):e1006376. https://doi.org/10.1371/journal.pcbi.1006376
  41. Dey D, Gaur S, Ovrehus KA, Slomka PJ, Betancur J, Goeller M, et al. Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study. Eur Radiol. 2018;28(6):2655-64. https://doi.org/10.1007/s00330-017-5223-z
  42. Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng. 2018;2(3):158-64. https://doi.org/10.1038/s41551-018-0195-0
  43. Sharafi SM, Sylvestre JP, Chevrefils C, Soucy JP, Beaulieu S, Pascoal TA, et al. Vascular retinal biomarkers improves the detection of the likely cerebral amyloid status from hyperspectral retinal images. Alzheimers Dement (N Y). 2019;5:610-7. https://doi.org/10.1016/j.trci.2019.09.006
  44. Mitani A, Huang A, Venugopalan S, Corrado GS, Peng L, Webster DR, et al. Detection of anaemia from retinal fundus images via deep learning. Nat Biomed Eng. 2020;4(1):18-27. https://doi.org/10.1038/s41551-019-0487-z
  45. Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyo D, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med. 2018;24(10):1559-67. https://doi.org/10.1038/s41591-018-0177-5
  46. Kather JN, Pearson AT, Halama N, Jager D, Krause J, Loosen SH, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med. 2019;25(7):1054-6. https://doi.org/10.1038/s41591-019-0462-y
  47. Hosny A, Parmar C, Coroller TP, Grossmann P, Zeleznik R, Kumar A, et al. Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study. PLoS Med. 2018;15(11):e1002711. https://doi.org/10.1371/journal.pmed.1002711
  48. Lu H, Arshad M, Thornton A, Avesani G, Cunnea P, Curry E, et al. A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecularphenotypes of epithelial ovarian cancer. Nat Commun. 2019;10(1):764. https://doi.org/10.1038/s41467-019-08718-9
  49. Mesko B. FDA approvals for smart algorithms in medicine in one giant infographic [Internet]. [place unknown]: The Medical Futurist; 2019 [cited 2020 Jan 10]. Available from: https://medicalfuturist.com/fdaapprovals-for-algorithms-in-medicine.
  50. Stark A. FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems [Internet]. Silver Spring (MD): The U.S. Food and Drug Administration; 2018 [cited 2020 Jan 10]. Available from: https://www.fda.gov/news-events/press-announcements/fda-permits-marketing-artificial-intelligence-based-device-detect-certaindiabetes-related-eye.
  51. Elfin D. Philips to offer Paige.AI's prostate cancer detection tech [Internet]. Washington (DC): MEDTECHDIVE; 2019 [cited 2020 Jan 10]. Available from: https://www.medtechdive.com/news/philips-tooffer- paigeais-prostate-cancer-detection-tech/568514.
  52. Stein A. Zebra Medical Vision's solutions now available on Philips IntelliSpace AI workflow suite, enabling more AI capabilities for radiologists [Internet]. San Francisco (CA): Businesswire; 2019 [cited 2020 Jan 10]. Available from: https://www.businesswire.com/news/home/20191203005716/en/Zebra-Medical-Vision%E2%80%99s-Solutions-Philips-IntelliSpace-AI.
  53. Kincaid E. FDA clears GE Healthcare's AI triage algorithm on X-ray device [Internet]. New York (NY): Medscape; 2019 [cited 2020 Jan 10]. Available from: https://www.medscape.com/viewarticle/918340.
  54. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347-58. https://doi.org/10.1056/NEJMra1814259
  55. Somashekhar SP, Sepulveda MJ, Puglielli S, Norden AD, Shortliffe EH, Rohit Kumar C, et al. Watson for oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Ann Oncol. 2018;29(2):418-23. https://doi.org/10.1093/annonc/mdx781
  56. Liu C, Liu X, Wu F, Xie M, Feng Y, Hu C. Using artificial intelligence (Watson for oncology) for treatment recommendations amongst Chinese patients with lung cancer: feasibility study. J Med Internet Res. 2018;20(9):e11087. https://doi.org/10.2196/11087
  57. Pantuck AJ, Lee DK, Kee T, Wang P, Lakhotia S, Silverman MH, et al. Modulating BET bromodomain inhibitor ZEN-3694 and enzalutamide combination dosing in a metastatic prostate cancer patient using CURATE. AI, an artificial intelligence platform. Adv Therap. 2018;1(6):1800104. https://doi.org/10.1002/adtp.201800104
  58. Nikolov S, Blackwell S, Mendes R, De Fauw J, Meyer C, Hughes C, et al. Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy [Internet]. Ithaca (NY): arXiv, Cornell University; 2018 [cited 2020 Jan 10]. Available from: https://arxiv.org/abs/1809.04430.
  59. Sennaar K. Machine learning in surgical robotics: 4 applications that matter [Internet]. Newton (MA): EMERJ; 2019 [cited 2020 Jan 10]. Available from: https://emerj.com/ai-sector-overviews/machine-learningin-surgical-robotics-4-applications.
  60. Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268(1):70-6. https://doi.org/10.1097/SLA.0000000000002693
  61. Wang Z, Fey AM. SATR-DL: improving surgical skill assessment and task recognition in robot-assisted surgery with deep neural networks. Conf Proc IEEE Eng Med Biol Soc. 2018;2018:1793-6.
  62. Bain EE, Shafner L, Walling DP, Othman AA, Chuang-Stein C, Hinkle J, et al. Use of a novel artificial intelligence platform on mobile devices to assess dosing compliance in a phase 2 clinical trial in subjects with schizophrenia. JMIR Mhealth Uhealth. 2017;5(2):e18. https://doi.org/10.2196/mhealth.7030
  63. Eggerth A, Hayn D, Schreier G. Medication management needs information and communications technology-based approaches, including telehealth and artificial intelligence. Br J Clin Pharmacol. 2019 Jul 4 [Epub]. https://doi.org/10.1111/bcp.14045.
  64. Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 2018;1:18. https://doi.org/10.1038/s41746-018-0029-1
  65. Liang H, Tsui BY, Ni H, Valentim CC, Baxter SL, Liu G, et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med. 2019;25(3):433-8. https://doi.org/10.1038/s41591-018-0335-9
  66. Tomasev N, Glorot X, Rae JW, Zielinski M, Askham H, Saraiva A, et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature. 2019;572(7767):116-9. https://doi.org/10.1038/s41586-019-1390-1
  67. Safavi KC, Khaniyev T, Copenhaver M, Seelen M, Zenteno Langle AC, Zanger J, et al. Development and validation of a machine learning model to aid discharge processes for inpatient surgical care. JAMA Netw Open. 2019;2(12):e1917221. https://doi.org/10.1001/jamanetworkopen.2019.17221
  68. Van Steenkiste T, Ruyssinck J, De Baets L, Decruyenaere J, De Turck F, Ongenae F, et al. Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks. Artif Intell Med. 2019;97:38-43. https://doi.org/10.1016/j.artmed.2018.10.008
  69. Alaa AM, Bolton T, Di Angelantonio E, Rudd JH, van der Schaar M. Cardiovascular disease risk prediction using automated machine learning: a prospective study of 423,604 UK Biobank participants. PLoS One. 2019;14(5):e0213653. https://doi.org/10.1371/journal.pone.0213653
  70. Kwon JM, Lee Y, Lee Y, Lee S, Park J. An algorithm based on deep learning for predicting in-hospital cardiac arrest. J Am Heart Assoc. 2018;7(13):e008678. https://doi.org/10.1161/JAHA.118.008678
  71. Meiring C, Dixit A, Harris S, MacCallum NS, Brealey DA, Watkinson PJ, et al. Optimal intensive care outcome prediction over time using machine learning. PLoS One. 2018;13(11):e0206862. https://doi.org/10.1371/journal.pone.0206862
  72. Tison GH, Sanchez JM, Ballinger B, Singh A, Olgin JE, Pletcher MJ, et al. Passive detection of atrial fibrillation using a commercially available smartwatch. JAMA Cardiol. 2018;3(5):409-16. https://doi.org/10.1001/jamacardio.2018.0136
  73. Perez MV, Mahaffey KW, Hedlin H, Rumsfeld JS, Garcia A, Ferris T, et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med. 2019;381(20):1909-17. https://doi.org/10.1056/NEJMoa1901183
  74. Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim HC, et al. Artificial intelligence for mental health and mental illnesses: an overview. Curr Psychiatry Rep. 2019;21(11):116. https://doi.org/10.1007/s11920-019-1094-0
  75. Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079-94. https://doi.org/10.1016/j.cell.2015.11.001
  76. Kent J. Amazon introduces machine learning medical transcription service [Internet]. Danvers (MA): HealthITAnalytics; 2019 [cited 2020 Jan 10]. Available from: https://healthitanalytics.com/news/amazon-introducesmachine-learning-medical-transcription-service.
  77. Maier-Hein L, Vedula SS, Speidel S, Navab N, Kikinis R, Park A, et al. Surgical data science for next-generation interventions. Nat Biomed Eng. 2017;1(9):691-6. https://doi.org/10.1038/s41551-017-0132-7
  78. Zhu H. Big data and artificial intelligence modeling for drug discovery. Annu Rev Pharmacol Toxicol. 2020;60:573-89. https://doi.org/10.1146/annurev-pharmtox-010919-023324
  79. Basile AO, Yahi A, Tatonetti NP. Artificial intelligence for drug toxicity and safety. Trends Pharmacol Sci. 2019;40(9):624-35. https://doi.org/10.1016/j.tips.2019.07.005
  80. Harrer S, Shah P, Antony B, Hu J. Artificial intelligence for clinical trial design. Trends Pharmacol Sci. 2019;40(8):577-91. https://doi.org/10.1016/j.tips.2019.05.005
  81. Sumitomo Dainippon Pharma: discovering and designing drugs with artificial intelligence [Internet]. Annecy: MarketScreener; 2020 [cited 2020 Jan 10]. Available from: https://www.marketscreener.com/SUMITOMODAINIPPON-PHARMA-6492512/news/Sumitomo-Dainippon-Pharma-Discovering-and-designing-drugs-with-artificial-intelligence-30106469/.
  82. Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, et al. A deep learning approach to antibiotic discovery. Cell. 2020;180(4):688-702. https://doi.org/10.1016/j.cell.2020.01.021
  83. Van Veen F. The neural network zoo [Internet]. Utrecht: The Asimov Institute; 2016 [cited 2020 Jan 10]. Available from: https://www.asimovinstitute.org/neural-network-zoo/.
  84. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial networks [Internet]. Ithaca (NY): arXiv, Cornell University; 2014 [cited 2020 Jan 10]. Available from: https://arxiv.org/pdf/1406.2661.pdf.
  85. Borkowski AA, Wilson CP, Borkowski SA, Thomas LB, Deland LA, Grewe SJ, et al. Comparing artificial intelligence platforms for histopathologic cancer diagnosis. Fed Pract. 2019;36(10):456-63.
  86. Faes L, Wagner SK, Fu DJ, Liu X, Korot E, Ledsam JR, et al. Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study. Lancet Digit Health. 2019;1(5):e232-42. https://doi.org/10.1016/S2589-7500(19)30108-6
  87. Schulte F, Fry E. No safety switch: how lax oversight of electronic health records puts patients at risk [Internet]. [place unknown]: Kaiser Health News; 2019 [cited 2020 Jan 10]. Available from: https://khn.org/news/no-safety-switch-how-lax-oversight-of-electronic-health-recordsputs-patients-at-risk.
  88. Dastin J. Amazon scraps secret AI recruiting tool that showed bias against women [Internet]. New York (NY): Reuters; 2018 [cited 2020 Jan 10]. Available from: https://www.reuters.com/article/us-amazoncom-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G.
  89. Simonite T. When it comes to gorillas, Google photos remains blind [Internet]. Boone (IA): Weird; 2018 [cited 2020 Jan 10]. Available from: https://www.wired.com/story/when-it-comes-to-gorillas-google-photos-remains-blind/.
  90. Polonski V. AI is convicting criminals and determining jail time, but is it fair? [Internet]. Cologny: World Economic Forum; 2018 [cited 2020 Jan 10]. Available from: https://www.weforum.org/agenda/2018/11/algorithms-court-criminals-jail-time-fair/.
  91. Lashbrook A. AI-driven dermatology could leave dark-skinned patients behind [Internet]. [place unknown]: The Atlantic; 2018 [cited 2020 Jan 10]. Available from: https://www.theatlantic.com/health/archive/2018/08/machine-learning-dermatology-skin-color/567619/.
  92. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-53. https://doi.org/10.1126/science.aax2342
  93. Arrieta AB, Diaz-Rodriguez N, Ser JD, Bennetot A, Tabik S, Barbado A, et al. Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI [Internet]. Ithaca (NY): arXiv, Cornell University; 2019 [cited 2020 Jan 10]. Available from: https://arxiv.org/abs/1910.10045.
  94. Murdoch WJ, Singh C, Kumbier K, Abbasi-Asl R, Yu B. Interpretable machine learning: definitions, methods, and applications [Internet]. Ithaca (NY): arXiv, Cornell University; 2019 [cited 2020 Jan 10]. Available from: https://arxiv.org/abs/1901.04592.
  95. Matheny M, Israni ST, Auerbach A, Beam A, Bleicher P, Chapman W, et al. Artificial intelligence in health care: the hope, the hype, the promise, the peril [Internet]. Washington (DC): National Academy of Medicine; 2019 [cited 2020 Jan 10]. Available from: https://nam.edu/artificial-intelligence-special-publication/.
  96. Bender E. Unpacking the black box in artificial intelligence for medicine [Internet]. Cambridge (MA): Undark; 2019 [cited 2020 Jan 10]. Available from: https://undark.org/2019/12/04/black-box-artificial-intelligence/.
  97. Wang F, Kaushal R, Khullar D. Should health care demand interpretable artificial intelligence or accept "black box" medicine? Ann Intern Med. 2020;172(1):59-60. https://doi.org/10.7326/m19-2548
  98. 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(11):e1002683. https://doi.org/10.1371/journal.pmed.1002683
  99. Winkler JK, Fink C, Toberer F, Enk A, Deinlein T, Hofmann-Wellenhof R, et al. 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(10):1135-41. https://doi.org/10.1001/jamadermatol.2019.1735
  100. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8. https://doi.org/10.1038/nature21056
  101. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17(1):195. https://doi.org/10.1186/s12916-019-1426-2
  102. Raumviboonsuk P, Krause J, Chotcomwongse P, Sayres R, Raman R, Widner K, et al. Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program. NPJ Digit Med. 2019;2:25. https://doi.org/10.1038/s41746-019-0099-8
  103. Schinkel M, Paranjape K, Nannan Panday RS, Skyttberg N, Nanayakkara PW. Clinical applications of artificial intelligence in sepsis: a narrative review. Comput Biol Med. 2019;115:103488. https://doi.org/10.1016/j.compbiomed.2019.103488
  104. Liu Y, Chen PC, Krause J, Peng L. How to read articles that use machine learning: users' guides to the medical literature. JAMA. 2019;322(18):1806-16. https://doi.org/10.1001/jama.2019.16489
  105. Doshi-Velez F, Perlis RH. Evaluating machine learning articles. JAMA. 2019;322(18):1777-9. https://doi.org/10.1001/jama.2019.17304
  106. Collins GS, Moons KG. Reporting of artificial intelligence prediction models. Lancet. 2019;393(10181):1577-9. https://doi.org/10.1016/S0140-6736(19)30037-6
  107. Adamson AS, Welch HG. Machine learning and the cancer-diagnosis problem: no gold standard. N Engl J Med. 2019;381(24):2285-7. https://doi.org/10.1056/NEJMp1907407
  108. Marcus GM. The Apple Watch can detect atrial fibrillation: so what now? Nat Rev Cardiol. 2020;17(3):135-6. https://doi.org/10.1038/s41569-019-0330-y
  109. Digital Health Software Precertification (Pre-Cert) Program [Internet]. Silver Spring (MD): U.S. Food & Drug Administration; 2019 [cited 2020 Jan 10]. Available from: https://www.fda.gov/medical-devices/digital-health/digital-health-software-precertification-pre-cert-program.
  110. Szabo L. Artificial intelligence is rushing into patient care: and could raise risks [Internet]. New York (NY): Scientific American; 2019 [cited 2020 Jan 10]. Available from: https://www.scientificamerican.com/article/artificial-intelligence-is-rushing-into-patient-care-and-could-raise-risks/.
  111. Lee TT, Kesselheim AS. U.S. Food and Drug Administration precertification pilot program for digital health software: weighing the benefits and risks. Ann Intern Med. 2018;168(10):730-2. https://doi.org/10.7326/m17-2715
  112. Wakabayashi D. Google and the University of Chicago are sued over data sharing [Internet]. New York (NY): The New York Times; 2019 [cited 2020 Jan 10]. Available from: https://www.nytimes.com/2019/06/26/technology/google-university-chicago-data-sharing-lawsuit.html.
  113. Singapore healthcare cyberattack: officials will not name hackers who targeted Prime Minister Lee Hsien Loong [Internet]. Hong Kong: South China Morning Post; 2019 [cited 2020 Jan 10]. Available from: https://www.scmp.com/news/asia/southeast-asia/article/2182222/singapore-healthcare-cyberattack-officials-will-not-name.
  114. Finlayson SG, Bowers JD, Ito J, Zittrain JL, Beam AL, Kohane IS. Adversarial attacks on medical machine learning. Science. 2019;363(6433):1287-9. https://doi.org/10.1126/science.aaw4399
  115. Heaven D. Why deep-learning AIs are so easy to fool. Nature. 2019;574(7777):163-6. https://doi.org/10.1038/d41586-019-03013-5
  116. Price WN 2nd, Gerke S, Cohen IG. Potential liability for physicians using artificial intelligence. JAMA. 2019 Oct 4 [Epub]. https://doi.org/10.1001/jama.2019.15064.
  117. Academy of Medical Royal Colleges. Artificial intelligence in healthcare [Internet]. London: Academy of Medical Royal Colleges; 2019 [cited 2020 Jan 10]. Available from: https://www.aomrc.org.uk/reportsguidance/artificial-intelligence-in-healthcare/.
  118. Char DS, Shah NH, Magnus D. Implementing machine learning in health care: addressing ethical challenges. N Engl J Med. 2018;378(11):981-3. https://doi.org/10.1056/NEJMp1714229
  119. The top 12 health chatbots [Internet]. [place unknown]: The Medical Futurist; 2020 [cited 2020 Jan 10]. Available from: https://medicalfuturist.com/top-12-health-chatbots/.
  120. Kiani A, Uyumazturk B, Rajpurkar P, Wang A, Gao R, Jones E, et al. Impact of a deep learning assistant on the histopathologic classification of liver cancer. NPJ Digit Med. 2020;3:23. https://doi.org/10.1038/s41746-020-0232-8
  121. Wartman SA, Combs CD. Medical education must move from the information age to the age of artificial intelligence. Acad Med. 2018;93(8):1107-9. https://doi.org/10.1097/acm.0000000000002044
  122. World Economic Forum. 2015 New vision for education unlocking the potential of technology [Internet]. Cologny: World Economic Forum; 2015 [cited 2020 Jan 10]. Available from: http://www.weforum.org/docs/WEFUSA_NewVisionforEducation_Report2015.pdf.
  123. Shah NR, Lee TH. What AI means for doctors and doctoring [Internet]. Waltham (MA): NEJM Catalyst; 2019 [cited 2020 Jan 10]. Available from: https://catalyst.nejm.org/doi/full/10.1056/CAT.19.0622.
  124. Kwon YJ, Kim CH, Nam IH, Yoo SW, Jung JS. Current status of artificial intelligence in healthcare and awareness survey of medical students on artificial intelligence: results report for biomedical research course in College of Medicine, Pusan National University. Busan: Pusan National University; 2018.
  125. Minor LB. The rise of the data-driven physician [Internet]. Stanford (CA): Stanford Medicine; 2020 [cited 2020 Jan 10]. Available from: http://med.stanford.edu/content/dam/sm/school/documents/Health-Trends-Report/Stanford%20Medicine%20Health%20Trends%20Report%202020.pdf.
  126. Murphy B. AMA: take extra care when applying AI in medical education [Internet]. Chicago (IL): American Medical Association; 2019 [cited 2020 Jan 10]. Available from: https://www.ama-assn.org/practicemanagement/digital/ama-take-extra-care-when-applying-ai-medical-education.
  127. NHS England. The Topol Review [Internet]. Leeds: NHS England [cited 2020 Jan 10]. Available from: https://topol.hee.nhs.uk/the-topol-review/.
  128. Minor L. Tomorrow's doctors seek training in data science, but will that be enough? [Internet]. Chicago (IL): Modern Healthcare; 2020 [cited 2020 Jan 10]. Available from: https://www.modernhealthcare.com/opinion-editorial/tomorrows-doctors-seek-training-data-sciencewill-be-enough.
  129. American Medical Association. Innovations & outcomes of the consortium [Internet]. Chicago (IL): American Medical Association; [cited 2020 Jan 10]. Available from: https://www.ama-assn.org/education/accelerating-change-medical-education/innovations-outcomes-consortium.
  130. Murphy B. 4 Phases to making goal of lifelong physician learner a reality [Internet]. Chicago (IL): American Medical Association; 2017 [cited 2020 Jan 10]. Available from: https://www.ama-assn.org/education/accelerating-change-medical-education/4-phases-making-goal-lifelong-physician-learner.
  131. Cutrer WB, Miller B, Pusic MV, Mejicano G, Mangrulkar RS, Gruppen LD, et al. Fostering the development of master adaptive learners: a conceptual model to guide skill acquisition in medical education. Acad Med. 2017;92(1):70-5. https://doi.org/10.1097/ACM.0000000000001323
  132. Harvard Medical School. The future is now: medical education for the 21st century [Internet]. San Bruno (CA): Youtube; 2015 [cited 2020 Jan 10]. Available from: https://www.youtube.com/watch?v=MRc9i85R2sY.
  133. Pathways course info [Internet]. Boston (MA): Harvard Medical School; 2019 [cited 2020 Jan 10]. Available from: https://meded.hms.harvard.edu/pathways-course-descriptions.
  134. Johns Hopkins Medicine. Genes to society: a curriculum for the Johns Hopkins University School of Medicine: design and structure of the genes to society curriculum [Internet]. Baltimore (MD): Johns Hopkins Medicine [cited 2020 Jan 10]. Available from: https://www.hopkinsmedicine.org/som/curriculum/genes_to_society/curriculumoverview.html.
  135. Johns Hopkins Medicine. Genes to society: a curriculum for the Johns Hopkins University School of Medicine: translational science intersessions [Internet]. Baltimore (MD): Johns Hopkins Medicine [cited 2020 Jan 10]. Available from: https://www.hopkinsmedicine.org/som/curriculum/genes_to_society/year-three/translational-science-intersessions.html.
  136. Hosamani P, Osterberg L, Shafer A. INDE 297: reflections, research & advances in patient care [Internet]. Stanford (CA): Stanford Medicine; 2020 [cited 2020 Jan 10]. Available from: http://med.stanford.edu/md/discovery-curriculum/clerkships/reflections.html.
  137. Medical Education, The University of California San Francisco. Inquiry immersion [Internet]. San Francisco (CA): The University of California San Francisco [cited 2020 Jan 10]. Available from: https://meded.ucsf.edu/md-program/current-students/curriculum/foundations-1/inquiry-immersion#MiniCourse-Elements.
  138. American Medical Association. Learning analytics for training in the workplace [Internet]. San Bruno (CA): Youtube; 2015 [cited 2020 Jan 10]. Available from: https://www.youtube.com/watch?v=ykQ8qvlvtzM&feature=youtu.be.
  139. Medical Education, The University of California San Francisco. Core inquiry curriculum [Internet]. San Francisco (CA): The University of California San Francisco; c2019 [cited 2020 Jan 10]. Available from: https://meded.ucsf.edu/md-program/current-students/curriculum/foundations-1/core-inquiry-curriculum.
  140. Medical Education, The University of California San Francisco. Inquiry immersion [Internet]. San Francisco (CA): The University of California San Francisco; c2019 [cited 2020 Jan 10]. Available from: https://meded.ucsf.edu/md-program/current-students/curriculum/foundations-1/inquiry-immersion#MiniCourse-Elements.
  141. Medical Education, The University of California San Francisco. Deep explore [Internet]. San Francisco (CA): The University of California San Francisco; c2019 [cited 2020 Jan 10]. Available from: https://meded.ucsf.edu/md-program/current-students/curriculum/career-launch/deep-explore.
  142. Kelleher JD, Tierney B. What is data science? [Internet]. Cambridge (MA): MIT Press; 2018 [cited 2020 Jan 10]. Available from: https://ieeexplore.ieee.org/document/8544248.
  143. Kolachalama VB, Garg PS. Machine learning and medical education. NPJ Digit Med. 2018;1:54. https://doi.org/10.1038/s41746-018-0061-1
  144. Paranjape K, Schinkel M, Nannan Panday R, Car J, Nanayakkara P. Introducing artificial intelligence training in medical education. JMIR Med Educ. 2019;5(2):e16048. https://doi.org/10.2196/16048
  145. Kolachalama VB. Teaching: MS650: machine learning for biomedical applications [Internet]. Boston (MA): Boston University [cited 2020 Jan 10]. Available from: http://sites.bu.edu/vkola/teaching/.
  146. Harvard Medical School. AISC 610: computationally-enabled medicine [Internet]. Boston (MA): Harvard Medical School [cited 2020 Jan 10]. Available from: https://dbmi.hms.harvard.edu/education/courses/aisc-610.
  147. Kilic A. Artificial intelligence and machine learning in healthcare mini-elective: Spring 2020 [Internet]. Pittsburgh (PA): University of Pittsburgh School of Medicine; 2020 [cited 2020 Jan 10]. Available from: https://www.omed.pitt.edu/sites/default/files/artificial_intelligence_and_machine_learning_in_healthcare.pdf.
  148. Stanford University. Precision practice with big data BIOMEDIN 205: precision medicine and big data [Internet]. Stanford (CA): Stanford University; 2019 [cited 2020 Jan 10]. Available from: https://canvas.stanford.edu/courses/106497.
  149. Park SH, Do KH, Kim S, Park JH, Lim YS. What should medical students know about artificial intelligence in medicine? J Educ Eval Health Prof. 2019;16:18. https://doi.org/10.3352/jeehp.2019.16.18
  150. Topol E. Deep medicine: how artificial intelligence can make healthcare human again. New York (NY): Basic Books; 2019.
  151. Korean Institute of Medical Education and Evaluation. Post 2nd period medical education accreditation standards and regulations. Seoul: Korean Institute of Medical Education and Evaluation; 2010.
  152. Korean Medical Association. Korean doctor's role. Seoul: Korean Medical Association; 2014.
  153. Korean Institute of Medical Education and Evaluation. Accreditation Standards of KIMEE 2019. Seoul: Korean Institute of Medical Education and Evaluation; 2019.
  154. Seo JH, Kim HJ, Kim BJ, Lee SJ, Bae HO. Educational and relational stressors associated with burnout in korean medical students. Psychiatry Investig. 2015;12(4):451-8. https://doi.org/10.4306/pi.2015.12.4.451
  155. Dyrbye LN, Massie FS Jr, Eacker A, Harper W, Power D, Durning SJ, et al. Relationship between burnout and professional conduct and attitudes among US medical students. JAMA. 2010;304(11):1173-80. https://doi.org/10.1001/jama.2010.1318
  156. Samra R. Empathy and burnout in medicine-acknowledging risks and opportunities. J Gen Intern Med. 2018;33(7):991-3. https://doi.org/10.1007/s11606-018-4443-5
  157. NHS England. Digital fellowships [Internet]. Leeds: NHS England; 2019 [cited 2020 Jan 10]. Available from: https://topol.hee.nhs.uk/digital-fellowships/.
  158. Boyko O, Chang A. American Board of Artificial Intelligence in Medicine (ABAIM) aims to educate and certify healthcare professionals in AI, and related technologies [Internet]. Beltsville (MD): CISION PRWeb; 2020 [cited 2020 Mar 10]. Available from: https://www.prweb.com/releases/american_board_of_artificial_intelligence_in_medicine_abaim_aims_to_educate_and_certify_healthcare_professionals_in_ai_and_related_technologies/prweb16963954.htm.