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의료분야에서 인공지능 현황 및 의학교육의 방향

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

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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.

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.

참고문헌 (158)

  1. Lexico, "Meaning of artificial intelligence in English" (2020), Lexico, Oxford University Press
  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, 141(2), pp.175~215 (2004)
  3. McCarthy J, "A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955", AI Mag, 27(4), pp.12~ (2006)
  4. Rosenblatt F, "The perceptron : a probabilistic model for information storage and organization in the brain", Psychol Rev, 65(6), pp.386~408 (1958)
  5. Rumelhart DE, "Learning representations by back-propagating errors", Nature, 323(6088), pp.533~536 (1986)
  6. Hinton GE, "A fast learning algorithm for deep belief nets", Neural Comput, 18(7), pp.1527~1554 (2006)
  7. Le Cun Y, "Artificial neural networks", pp.463~468 (1992), IEEE Press
  8. Krizhevsky A, "ImageNet classification with deep convolutional neural networks", Adv Neural Inf Process Syst, 25, pp.1097~1105 (2012)
  9. "Building high-level features using large scale unsupervised learning" (2012)[URL]
  10. Hinton G, "Deep neural networks for acoustic modeling in speech recognition : the shared views of four research groups", IEEE Signal Process Mag, 29(6), pp.82~97 (2012)
  11. Silver D, "Mastering the game of Go with deep neural networks and tree search", Nature, 529(7587), pp.484~489 (2016)
  12. Gulshan V, "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs", JAMA, 316(22), pp.2402~2410 (2016)
  13. Singh H, "The frequency of diagnostic errors in outpatient care : estimations from three large observational studies involving US adult populations", BMJ Qual Saf, 23(9), pp.727~731 (2014)
  14. Balogh EP, "Improving diagnosis in health care" (2015), National Academies Press
  15. Dewa CS, "The relationship between physician burnout and quality of healthcare in terms of safety and acceptability : a systematic review", BMJ Open, 7(6), pp.e015141~ (2017)
  16. "Will artificial intelligence replace doctors?" (2019)[URL]
  17. Topol EJ, "High-performance medicine : the convergence of human and artificial intelligence", Nat Med, 25(1), pp.44~56 (2019)
  18. Coiera E, "The fate of medicine in the time of AI", Lancet, 392(10162), pp.2331~2332 (2018)
  19. Davenport T, "The potential for artificial intelligence in healthcare", Future Healthc J, 6(2), pp.94~98 (2019)
  20. Gong B, "Influence of artificial intelligence on canadian medical students' preference for radiology specialty : a national survey study", Acad Radiol, 26(4), pp.566~577 (2019)
  21. Choy G, "Current applications and future impact of machine learning in radiology", Radiology, 288(2), pp.318~328 (2018)
  22. Yala A, "A deep learning mammography-based model for improved breast cancer risk prediction", Radiology, 292(1), pp.60~66 (2019)
  23. Bera K, "Artificial intelligence in digital pathology : new tools for diagnosis and precision oncology", Nat Rev Clin Oncol, 16(11), pp.703~715 (2019)
  24. Chen PC, "An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis", Nat Med, 25(9), pp.1453~1457 (2019)
  25. Luo H, "Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy : a multicentre, case-control, diagnostic study", Lancet Oncol, 20(12), pp.1645~1654 (2019)
  26. Mori Y, "Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy : a prospective study", Ann Intern Med, 169(6), pp.357~366 (2018)
  27. Le Berre C, "Application of artificial intelligence to gastroenterology and hepatology", Gastroenterology, 158(1), pp.76~94 (2020)
  28. Thomsen K, "Systematic review of machine learning for diagnosis and prognosis in dermatology", J Dermatolog Treat, pp.1~15 (2019)
  29. Dick V, "Accuracy of computer-aided diagnosis of melanoma : a meta-analysis", JAMA Dermatol, 155(11), pp.1291~1299 (2019)
  30. Cuocolo R, "Current applications of big data and machine learning in cardiology", J Geriatr Cardiol, 16(8), pp.601~607 (2019)
  31. Ting DS, "Deep learning in ophthalmology : the technical and clinical considerations", Prog Retin Eye Res, 72, pp.100759~ (2019)
  32. De Fauw J, "Clinically applicable deep learning for diagnosis and referral in retinal disease", Nat Med, 24(9), pp.1342~1350 (2018)
  33. Liu X, "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, 1(6), pp.e271~e297 (2019)
  34. Ardila D, "End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography", Nat Med, 25(6), pp.954~961 (2019)
  35. McKinney SM, "International evaluation of an AI system for breast cancer screening", Nature, 577(7788), pp.89~94 (2020)
  36. Liu Y, "A deep learning system for differential diagnosis of skin diseases", Nat Med (2020)[DOI]
  37. Majkowska A, "Chest radiograph interpretation with deep learning models : assessment with radiologist-adjudicated reference standards and population-adjusted evaluation", Radiology, 294(2), pp.421~431 (2020)
  38. Hwang EJ, "Deep learning for chest radiograph diagnosis in the emergency department", Radiology, 293(3), pp.573~580 (2019)
  39. Hsieh TC, "PEDIA : prioritization of exome data by image analysis", Genet Med, 21(12), pp.2807~2814 (2019)
  40. Bhagwat N, "Modeling and prediction of clinical symptom trajectories in Alzheimer’s disease using longitudinal data", PLoS Comput Biol, 14(9), pp.e1006376~ (2018)
  41. Dey D, "Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning : a multicentre study", Eur Radiol, 28(6), pp.2655~2664 (2018)
  42. Poplin R, "Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning", Nat Biomed Eng, 2(3), pp.158~164 (2018)
  43. Sharafi SM, "Vascular retinal biomarkers improves the detection of the likely cerebral amyloid status from hyperspectral retinal images", Alzheimers Dement(N Y), 5, pp.610~617 (2019)
  44. Mitani A, "Detection of anaemia from retinal fundus images via deep learning", Nat Biomed Eng, 4(1), pp.18~27 (2020)
  45. Coudray N, "Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning", Nat Med, 24(10), pp.1559~1567 (2018)
  46. Kather JN, "Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer", Nat Med, 25(7), pp.1054~1056 (2019)
  47. Hosny A, "Deep learning for lung cancer prognostication : a retrospective multi-cohort radiomics study", PLoS Med, 15(11), pp.e1002711~ (2018)
  48. Lu H, "A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic-and molecularphenotypes of epithelial ovarian cancer", Nat Commun, 10(1), pp.764~ (2019)
  49. "FDA approvals for smart algorithms in medicine in one giant infographic" (2019)[URL]
  50. "FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems" (2018)[URL]
  51. "Philips to offer Paige.AI’s prostate cancer detection tech" (2019)[URL]
  52. "Zebra Medical Vision’s solutions now available on Philips IntelliSpace AI workflow suite, enabling more AI capabilities for radiologists" (2019)[URL]
  53. "FDA clears GE Healthcare’s AI triage algorithm on X-ray device" (2019)[URL]
  54. Rajkomar A, "Machine learning in medicine", N Engl J Med, 380(14), pp.1347~1358 (2019)
  55. Somashekhar SP, "Watson for oncology and breast cancer treatment recommendations : agreement with an expert multidisciplinary tumor board", Ann Oncol, 29(2), pp.418~423 (2018)
  56. Liu C, "Using artificial intelligence(Watson for oncology)for treatment recommendations amongst Chinese patients with lung cancer : feasibility study", J Med Internet Res, 20(9), pp.e11087~ (2018)
  57. Pantuck AJ, "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, 1(6), pp.1800104~ (2018)
  58. "Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy" (2018)[URL]
  59. "Machine learning in surgical robotics: 4 applications that matter" (2019)[URL]
  60. Hashimoto DA, "Artificial intelligence in surgery : promises and perils", Ann Surg, 268(1), pp.70~76 (2018)
  61. Wang Z, "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, pp.1793~1796 (2018)
  62. Bain EE, "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, 5(2), pp.e18~ (2017)
  63. Eggerth A, "Medication management needs information and communications technology-based approaches, including telehealth and artificial intelligence", Br J Clin Pharmacol (2019)[DOI]
  64. Rajkomar A, "Scalable and accurate deep learning with electronic health records", NPJ Digit Med, 1, pp.18~ (2018)
  65. Liang H, "Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence", Nat Med, 25(3), pp.433~438 (2019)
  66. Tomasev N, "A clinically applicable approach to continuous prediction of future acute kidney injury", Nature, 572(7767), pp.116~119 (2019)
  67. Safavi KC, "Development and validation of a machine learning model to aid discharge processes for inpatient surgical care", JAMA Netw Open, 2(12), pp.e1917221~ (2019)
  68. Van Steenkiste T, "Accurate prediction of blood culture outcome in the intensive care unit using long short-term memory neural networks", Artif Intell Med, 97, pp.38~43 (2019)
  69. Alaa AM, "Cardiovascular disease risk prediction using automated machine learning : a prospective study of 423, 604 UK Biobank participants", PLoS One, 14(5), pp.e0213653~ (2019)
  70. Kwon JM, "An algorithm based on deep learning for predicting in-hospital cardiac arrest", J Am Heart Assoc, 7(13), pp.e008678~ (2018)
  71. Meiring C, "Optimal intensive care outcome prediction over time using machine learning", PLoS One, 13(11), pp.e0206862~ (2018)
  72. Tison GH, "Passive detection of atrial fibrillation using a commercially available smartwatch", JAMA Cardiol, 3(5), pp.409~416 (2018)
  73. Perez MV, "Large-scale assessment of a smartwatch to identify atrial fibrillation", N Engl J Med, 381(20), pp.1909~1917 (2019)
  74. Graham S, "Artificial intelligence for mental health and mental illnesses : an overview", Curr Psychiatry Rep, 21(11), pp.116~ (2019)
  75. Zeevi D, "Personalized nutrition by prediction of glycemic responses", Cell, 163(5), pp.1079~1094 (2015)
  76. "Amazon introduces machine learning medical transcription service" (2019)[URL]
  77. Maier-Hein L, "Surgical data science for next-generation interventions", Nat Biomed Eng, 1(9), pp.691~696 (2017)
  78. Zhu H, "Big data and artificial intelligence modeling for drug discovery", Annu Rev Pharmacol Toxicol, 60, pp.573~589 (2020)
  79. Basile AO, "Artificial intelligence for drug toxicity and safety", Trends Pharmacol Sci, 40(9), pp.624~635 (2019)
  80. Harrer S, "Artificial intelligence for clinical trial design", Trends Pharmacol Sci, 40(8), pp.577~591 (2019)
  81. "Sumitomo Dainippon Pharma: discovering and designing drugs with artificial intelligence" (2020)[URL]
  82. Stokes JM, "A deep learning approach to antibiotic discovery", Cell, 180(4), pp.688~702 (2020)
  83. "The neural network zoo" (2016)[URL]
  84. "Generative adversarial networks" (2014)[URL]
  85. Borkowski AA, "Comparing artificial intelligence platforms for histopathologic cancer diagnosis", Fed Pract, 36(10), pp.456~463 (2019)
  86. Faes L, "Automated deep learning design for medical image classification by health-care professionals with no coding experience : a feasibility study", Lancet Digit Health, 1(5), pp.e232~e242 (2019)
  87. "No safety switch: how lax oversight of electronic health records puts patients at risk" (2019)[URL]
  88. "Amazon scraps secret AI recruiting tool that showed bias against women" (2018)[URL]
  89. "When it comes to gorillas, Google photos remains blind" (2018)[URL]
  90. "AI is convicting criminals and determining jail time, but is it fair?" (2018)[URL]
  91. "AI-driven dermatology could leave dark-skinned patients behind" (2018)[URL]
  92. Obermeyer Z, "Dissecting racial bias in an algorithm used to manage the health of populations", Science, 366(6464), pp.447~453 (2019)
  93. "Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI" (2019)[URL]
  94. "Interpretable machine learning: definitions, methods, and applications" (2019)[URL]
  95. "Artificial intelligence in health care: the hope, the hype, the promise, the peril" (2019)[URL]
  96. "Unpacking the black box in artificial intelligence for medicine" (2019)[URL]
  97. Wang F, "Should health care demand interpretable artificial intelligence or accept “black box” medicine?", Ann Intern Med, 172(1), pp.59~60 (2020)
  98. Zech JR, "Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs : a cross-sectional study", PLoS Med, 15(11), pp.e1002683~ (2018)
  99. Winkler JK, "Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition", JAMA Dermatol, 155(10), pp.1135~1141 (2019)
  100. Esteva A, "Dermatologist-level classification of skin cancer with deep neural networks", Nature, 542(7639), pp.115~118 (2017)
  101. Kelly CJ, "Key challenges for delivering clinical impact with artificial intelligence", BMC Med, 17(1), pp.195~ (2019)
  102. Raumviboonsuk P, "Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program", NPJ Digit Med, 2, pp.25~ (2019)
  103. Schinkel M, "Clinical applications of artificial intelligence in sepsis : a narrative review", Comput Biol Med, 115, pp.103488~ (2019)
  104. Liu Y, "How to read articles that use machine learning : users’ guides to the medical literature", JAMA, 322(18), pp.1806~1816 (2019)
  105. Doshi-Velez F, "Evaluating machine learning articles", JAMA, 322(18), pp.1777~1779 (2019)
  106. Collins GS, "Reporting of artificial intelligence prediction models", Lancet, 393(10181), pp.1577~1579 (2019)
  107. Adamson AS, "Machine learning and the cancer-diagnosis problem : no gold standard", N Engl J Med, 381(24), pp.2285~2287 (2019)
  108. Marcus GM, "The Apple Watch can detect atrial fibrillation: so what now?", Nat Rev Cardiol, 17(3), pp.135~136 (2020)
  109. "Digital Health Software Precertification (Pre-Cert) Program" (2019)[URL]
  110. "Artificial intelligence is rushing into patient care: and could raise risks" (2019)[URL]
  111. Lee TT, "U.S. Food and Drug Administration precertification pilot program for digital health software: weighing the benefits and risks", Ann Intern Med, 168(10), pp.730~732 (2018)
  112. "Google and the University of Chicago are sued over data sharing" (2019)[URL]
  113. "Singapore healthcare cyberattack: officials will not name hackers who targeted Prime Minister Lee Hsien Loong" (2019)[URL]
  114. Finlayson SG, "Adversarial attacks on medical machine learning", Science, 363(6433), pp.1287~1289 (2019)
  115. Heaven D, "Why deep-learning AIs are so easy to fool", Nature, 574(7777), pp.163~166 (2019)
  116. Price WN 2nd, "Potential liability for physicians using artificial intelligence", JAMA (2019)[DOI]
  117. "Artificial intelligence in healthcare" (2019)[URL]
  118. Char DS, "Implementing machine learning in health care : addressing ethical challenges", N Engl J Med, 378(11), pp.981~983 (2018)
  119. "The top 12 health chatbots" (2020)[URL]
  120. Kiani A, "Impact of a deep learning assistant on the histopathologic classification of liver cancer", NPJ Digit Med, 3, pp.23~ (2020)
  121. Wartman SA, "Medical education must move from the information age to the age of artificial intelligence", Acad Med, 93(8), pp.1107~1109 (2018)
  122. "2015 New vision for education unlocking the potential of technology" (2015)[URL]
  123. "What AI means for doctors and doctoring" (2019)[URL]
  124. 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, "Kwon YJ", Pusan National University, ~ (2018)
  125. "The rise of the data-driven physician" (2020)[URL]
  126. "AMA: take extra care when applying AI in medical education" (2019)[URL]
  127. "The Topol Review"[URL]
  128. "Tomorrow’s doctors seek training in data science, but will that be enough?" (2020)[URL]
  129. "Innovations & outcomes of the consortium"[URL]
  130. "4 Phases to making goal of lifelong physician learner a reality" (2017)[URL]
  131. Cutrer WB, "Fostering the development of master adaptive learners : a conceptual model to guide skill acquisition in medical education", Acad Med, 92(1), pp.70~75 (2017)
  132. "The future is now: medical education for the 21st century" (2015)[URL]
  133. "Pathways course info" (2019)[URL]
  134. "Genes to society: a curriculum for the Johns Hopkins University School of Medicine: design and structure of the genes to society curriculum"[URL]
  135. "Genes to society: a curriculum for the Johns Hopkins University School of Medicine: translational science intersessions"[URL]
  136. "INDE 297: reflections, research & advances in patient care" (2020)[URL]
  137. "Inquiry immersion"[URL]
  138. "Learning analytics for training in the workplace" (2015)[URL]
  139. "Core inquiry curriculum" (2019)[URL]
  140. "Inquiry immersion" (2019)[URL]
  141. "Deep explore" (2019)[URL]
  142. Kelleher JD, "What is data science?" (2018), MIT Press
  143. Kolachalama VB, "Machine learning and medical education", NPJ Digit Med, 1, pp.54~ (2018)
  144. Paranjape K, "Introducing artificial intelligence training in medical education", JMIR Med Educ, 5(2), pp.e16048~ (2019)
  145. "Teaching: MS650: machine learning for biomedical applications"[URL]
  146. "AISC 610: computationally-enabled medicine"[URL]
  147. "Artificial intelligence and machine learning in healthcare mini-elective: Spring 2020" (2020)[URL]
  148. "Precision practice with big data BIOMEDIN 205:precision medicine and big data" (2019)[URL]
  149. 박성호, "What should medical students know about artificial intelligence in medicine?", Journal of Educational Evaluation for Health Professions, 16, pp.1~6 (2019), 한국보건의료인국가시험원[DOI]
  150. Topol E, "Deep medicine : how artificial intelligence can make healthcare human again" (2019), Basic Books
  151. Korean Institute of Medical Education and Evaluation, "Post 2nd period medical education accreditation standards and regulations", Korean Institute of Medical Education and Evaluation, ~ (2010)
  152. Korean Medical Association, "Korean doctor’s role", Korean Medical Association, ~ (2014)
  153. Korean Institute of Medical Education and Evaluation, "Accreditation Standards of KIMEE 2019", Korean Institute of Medical Education and Evaluation, ~ (2019)
  154. 배화옥, "Educational and Relational Stressors Associated with Burnout in Korean Medical Students", PSYCHIATRY INVESTIGATION, 12(4), pp.451~458 (2015), 대한신경정신의학회[DOI]
  155. Dyrbye LN, "Relationship between burnout and professional conduct and attitudes among US medical students", JAMA, 304(11), pp.1173~1180 (2010)
  156. Samra R, "Empathy and burnout in medicine-acknowledging risks and opportunities", J Gen Intern Med, 33(7), pp.991~993 (2018)
  157. "Digital fellowships" (2019)[URL]
  158. "American Board of Artificial Intelligence in Medicine (ABAIM) aims to educate and certify healthcare professionals in AI, and related technologies" (2020)[URL]

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