과제정보
The authors acknowledge the participation of Korean Society of Neuroradiology (KSNR) in this survey.
참고문헌
- Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp 2018;2:35
- Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, et al. Deep learning: a primer for radiologists. Radiographics 2017;37:2113-2131 https://doi.org/10.1148/rg.2017170077
- Havaei M, Guizard N, Larochelle H, Jodoin PM. Deep learning trends for focal brain pathology segmentation in MRI. In: Holzinger A, ed. Machine learning for health informatics: state-of-the-art and future challenges. Cham: Springer, 2016:125-148
- Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S. Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 2016;35:1207-1216 https://doi.org/10.1109/TMI.2016.2535865
- Zhou LQ, Wang JY, Yu SY, Wu GG, Wei Q, Deng YB, et al. Artificial intelligence in medical imaging of the liver. World J Gastroenterol 2019;25:672-682 https://doi.org/10.3748/wjg.v25.i6.672
- 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 https://doi.org/10.3348/kjr.2021.0544
- Lee S, Shin HJ, Kim S, Kim EK. Successful implementation of an artificial intelligence-based computer-aided detection system for chest radiography in daily clinical practice. Korean J Radiol 2022;23:847-852 https://doi.org/10.3348/kjr.2022.0193
- Sakai K, Yamada K. Machine learning studies on major brain diseases: 5-year trends of 2014-2018. Jpn J Radiol 2019;37:34-72 https://doi.org/10.1007/s11604-018-0794-4
- Olthof AW, van Ooijen PMA, Rezazade Mehrizi MH. Promises of artificial intelligence in neuroradiology: a systematic technographic review. Neuroradiology 2020;62:1265-1278 https://doi.org/10.1007/s00234-020-02424-w
- van Leeuwen KG, Schalekamp S, Rutten MJCM, van Ginneken B, de Rooij M. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol 2021;31:3797-3804 https://doi.org/10.1007/s00330-021-07892-z
- Choi KS, Sunwoo L. Artificial intelligence in neuroimaging: clinical applications. Investig Magn Reson Imaging 2022;26:1-9 https://doi.org/10.13104/imri.2022.26.1.1
- Persson K, Barca ML, Cavallin L, Braekhus A, Knapskog AB, Selbaek G, et al. Comparison of automated volumetry of the hippocampus using NeuroQuant(R) and visual assessment of the medial temporal lobe in Alzheimer's disease. Acta Radiol 2018;59:997-1001 https://doi.org/10.1177/0284185117743778
- Sheth SA, Lopez-Rivera V, Barman A, Grotta JC, Yoo AJ, Lee S, et al. Machine learning-enabled automated determination of acute ischemic core from computed tomography angiography. Stroke 2019;50:3093-3100 https://doi.org/10.1161/STROKEAHA.119.026189
- Chilamkurthy S, Ghosh R, Tanamala S, Biviji M, Campeau NG, Venugopal VK, et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet 2018;392:2388-2396 https://doi.org/10.1016/S0140-6736(18)31645-3
- Yahav-Dovrat A, Saban M, Merhav G, Lankri I, Abergel E, Eran A, et al. Evaluation of artificial intelligence-powered identification of large-vessel occlusions in a comprehensive stroke center. AJNR Am J Neuroradiol 2021;42:247-254 https://doi.org/10.3174/ajnr.A6923
- Radboud UMC. Products. AI for Radiology.com Web site. https://grand-challenge.org/aiforradiology/. Accessed October 25, 2022
- Gallix B, Chong J. Artificial intelligence in radiology: who's afraid of the big bad wolf? Eur Radiol 2019;29:1637-1639 https://doi.org/10.1007/s00330-018-5995-9
- Huisman M, Ranschaert E, Parker W, Mastrodicasa D, Koci M, Pinto de Santos D, et al. An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude. Eur Radiol 2021;31:7058-7066 https://doi.org/10.1007/s00330-021-07781-5
- Coppola F, Faggioni L, Regge D, Giovagnoni A, Golfieri R, Bibbolino C, et al. Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey. Radiol Med 2021;126:63-71 https://doi.org/10.1007/s11547-020-01205-y
- 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: ANational survey study. Acad Radiol 2019;26:566-577 https://doi.org/10.1016/j.acra.2018.10.007
- Scheetz J, Rothschild P, McGuinness M, Hadoux X, Soyer HP, Janda M, et al. A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology. Sci Rep 2021;11:5193
- Shin SY. Current status and future direction of digital health in Korea. Korean J Physiol Pharmacol 2019;23:311-315 https://doi.org/10.4196/kjpp.2019.23.5.311
- Ministry of Food and Drug Safety. The Ministry of Food and Drug Safety has become a leading country in regulating AI medical devices. Ministry of Food and Drug Safety Web site. https://www.mfds.go.kr/brd/m_99/view.do?seq=46379&srchFr=&srchTo=&srchWord=&srchTp=&itm_seq_1=0&itm_seq_2=0&multi_itm_seq=0&company_cd=&company_nm=&page=1. Published May 22, 2022. Accessed September 24, 2022
- Perneger TV. What's wrong with Bonferroni adjustments. BMJ 1998;316:1236-1238 https://doi.org/10.1136/bmj.316.7139.1236
- Pinto Dos Santos D, Giese D, Brodehl S, Chon SH, Staab W, Kleinert R, et al. Medical students' attitude towards artificial intelligence: a multicentre survey. Eur Radiol 2019;29:1640-1646 https://doi.org/10.1007/s00330-018-5601-1
- European Society of Radiology. Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology. Insights Imaging 2019;10:105