참고문헌
- Ehteshami Bejnordi B, Veta M, Johannes van Diest P, 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;318:2199-2210
- 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;542:115-118
- 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;316:2402-2410
- Hwang EJ, Park S, Jin KN, Kim JI, Choi SY, Lee JH, et al. Development and validation of a deep learningbased automated detection algorithm for major thoracic diseases on chest radiographs. JAMA Netw Open 2019;2:e191095
- Nam JG, Park S, Hwang EJ, Lee JH, Jin KN, Lim KY, et al. Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology 2019;290:218-228
- Hwang EJ, Goo JM, Yoon SH, Beck KS, Seo JB, Choi BW, et al. Use of artificial intelligence-based software as medical devices for chest radiography: a position paper from the Korean Society of Thoracic Radiology. Korean J Radiol 2021;22:1743-1748
- Hwang EJ, Park CM. Clinical implementation of deep learning in thoracic radiology: potential applications and challenges. Korean J Radiol 2020;21:511-525
- Ministry of Food and Drug Safety. Medical device safety library. Available at. https://emedi.mfds.go.kr/search/data/MNU20237. Accessed June 14, 2024
- Hwang EJ, Park JE, Song KD, Yang DH, Kim KW, Lee JG, et al. 2023 survey on user experience of artificial intelligence software in radiology by the Korean Society of Radiology. Korean J Radiol 2024;25:613-622
- Allen B, Agarwal S, Coombs L, Wald C, Dreyer K. 2020 ACR data science institute artificial intelligence survey. J Am Coll Radiol 2021;18:1153-1159
- European Society of Radiology (ESR). Current practical experience with artificial intelligence in clinical radiology: a survey of the European Society of Radiology. Insights Imaging 2022;13:107
- Health Insurance Review and Assessment Service. Release of guideline for health insurance registration of digital treatment devices and artificial intelligence (AI). Available at. https://www.hira.or.kr/bbsDummy.do?pgmid=HIRAA020041000100&brdScnBltNo=4&brdBltNo=10957&pageIndex=1&pageIndex2=1. Published 2023. Accessed April 12, 2024
- Lunit Inc. Lunit INSIGHT CXR cleared approval as innovative medical technology. Available at. https://innovativecxr.lunit.io. Published 2023. Accessed April 12, 2024
- Hwang EJ, Park S, Jin KN, Kim JI, Choi SY, Lee JH, et al. Development and validation of a deep learning-based automatic detection algorithm for active pulmonary tuberculosis on chest radiographs. Clin Infect Dis 2019;69:739-747
- Nam JG, Kim M, Park J, Hwang EJ, Lee JH, Hong JH, et al. Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs. Eur Respir J 2021;57:2003061
- Lee JH, Sun HY, Park S, Kim H, Hwang EJ, Goo JM, et al. Performance of a deep learning algorithm compared with radiologic interpretation for lung cancer detection on chest radiographs in a health screening population. Radiology 2020;297:687-696
- Yoo H, Kim KH, Singh R, Digumarthy SR, Kalra MK. Validation of a deep learning algorithm for the detection of malignant pulmonary nodules in chest radiographs. JAMA Netw Open 2020;3:e2017135
- 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;293:573-580
- Gelaw SM, Kik SV, Ruhwald M, Ongarello S, Egzertegegne TS, Gorbacheva O, et al. Diagnostic accuracy of three computer-aided detection systems for detecting pulmonary tuberculosis on chest radiography when used for screening: analysis of an international, multicenter migrants screening study. PLOS Glob Public Health 2023;3:e0000402
- Kagujje M, Kerkhoff AD, Nteeni M, Dunn I, Mateyo K, Muyoyeta M. The performance of computer-aided detection digital chest X-ray reading technologies for triage of active tuberculosis among persons with a history of previous tuberculosis. Clin Infect Dis 2023;76:e894-e901
- Qin ZZ, Ahmed S, Sarker MS, Paul K, Adel ASS, Naheyan T, et al. Tuberculosis detection from chest X-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms. Lancet Digit Health 2021;3:e543-e554
- Nam JG, Hwang EJ, Kim J, Park N, Lee EH, Kim HJ, et al. AI improves nodule detection on chest radiographs in a health screening population: a randomized controlled trial. Radiology 2023;307:e221894
- Hong W, Hwang EJ, Lee JH, Park J, Goo JM, Park CM. Deep learning for detecting pneumothorax on chest radiographs after needle biopsy: clinical implementation. Radiology 2022;303:433-441
- Hwang EJ, Lee JS, Lee JH, Lim WH, Kim JH, Choi KS, et al. Deep learning for detection of pulmonary metastasis on chest radiographs. Radiology 2021;301:455-463
- Mongan J, Moy L, Kahn CE Jr. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell 2020;2:e200029
- Hwang EJ, Goo JM, Nam JG, Park CM, Hong KJ, Kim KH. Conventional versus artificial intelligence-assisted interpretation of chest radiographs in patients with acute respiratory symptoms in emergency department: a pragmatic randomized clinical trial. Korean J Radiol 2023;24:259-270
- Hong S, Hwang EJ, Kim S, Song J, Lee T, Jo GD, et al. Methods of visualizing the results of an artificial-intelligence-based computer-aided detection system for chest radiographs: effect on the diagnostic performance of radiologists. Diagnostics (Basel) 2023;13:1089
- Allen B, Dreyer K, Stibolt R Jr, Agarwal S, Coombs L, Treml C, et al. Evaluation and real-world performance monitoring of artificial intelligence models in clinical practice: try it, buy it, check it. J Am Coll Radiol 2021;18:1489-1496
- Brady AP, Allen B, Chong J, Kotter E, Kottler N, Mongan J, et al. Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA. J Am Coll Radiol 2024 Jan 23 [Epub]. https://doi.org/10.1016/j.jacr.2023.12.005
- Mello MM, Guha N. Understanding liability risk from using health care artificial intelligence tools. N Engl J Med 2024;390:271-278
- Jaremko JL, Azar M, Bromwich R, Lum A, Alicia Cheong LH, Gibert M, et al. Canadian Association of Radiologists white paper on ethical and legal issues related to artificial intelligence in radiology. Can Assoc Radiol J 2019;70:107-118
- Mezrich JL. Is artificial intelligence (AI) a pipe dream? Why legal issues present significant hurdles to AI autonomy. AJR Am J Roentgenol 2022;219:152-156
- O'Neill TJ, Xi Y, Stehel E, Browning T, Ng YS, Baker C, et al. Active reprioritization of the reading worklist using artificial intelligence has a beneficial effect on the turnaround time for interpretation of head CT with intracranial hemorrhage. Radiol Artif Intell 2020;3:e200024
- An JY, Hwang EJ, Nam G, Lee SH, Park CM, Goo JM, et al. Artificial intelligence for assessment of endotracheal tube position on chest radiographs: validation in patients from two institutions. AJR Am J Roentgenol 2024;222:e2329769
- Baltruschat I, Steinmeister L, Nickisch H, Saalbach A, Grass M, Adam G, et al. Smart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation. Eur Radiol 2021;31:3837-3845
- Koo HJ, Do KH. The staffing crisis and burnout in academic radiology: insights from a survey study in Korea. J Am Coll Radiol 2024;21:505-514
- Plesner LL, Muller FC, Nybing JD, Laustrup LC, Rasmussen F, Nielsen OW, et al. Autonomous chest radiograph reporting using AI: estimation of clinical impact. Radiology 2023;307:e222268
- Yoo H, Kim EY, Kim H, Choi YR, Kim MY, Hwang SH, et al. Artificial intelligence-based identification of normal chest radiographs: a simulation study in a multicenter health screening cohort. Korean J Radiol 2022;23:1009-1018
- Jung KH. Uncover this tech term: foundation model. Korean J Radiol 2023;24:1038-1041
- Fei N, Lu Z, Gao Y, Yang G, Huo Y, Wen J, et al. Towards artificial general intelligence via a multimodal foundation model. Nat Commun 2022;13:3094
- Huang J, Neill L, Wittbrodt M, Melnick D, Klug M, Thompson M, et al. Generative artificial intelligence for chest radiograph interpretation in the emergency department. JAMA Netw Open 2023;6:e2336100
- Yun J, Ahn Y, Cho K, Oh SY, Lee SM, Kim N, et al. Deep learning for automated triaging of stable chest radiographs in a follow-up setting. Radiology 2023;309:e230606