A Nationwide Web-Based Survey of Neuroradiologists’ Perceptions of Artificial Intelligence Software for Neuro-Applications in Korea
Open accessAbstract
Objective: We aimed to investigate current expectations and clinical adoption of artificial intelligence (AI) software among neuroradiologists in Korea. Materials and Methods:In April 2022, a 30-item online survey was conducted by neuroradiologists from the Korean Society of Neuroradiology (KSNR) to assess current user experiences, perceptions, attitudes, and future expectations regarding AI for neuro-applications.Respondents with experience in AI software were further investigated in terms of the number and type of software used, period of use, clinical usefulness, and future scope.Results were compared between respondents with and without experience with AI software through multivariable logistic regression and mediation analyses.Results: The survey was completed by 73 respondents, accounting for 21.9% (73/334) of the KSNR members; 72.6% (53/73) were familiar with AI and 58.9% (43/73) had used AI software, with approximately 86% (37/43) using 1-3 AI software programs and 51.2% (22/43) having up to one year of experience with AI software.Among AI software types, brain volumetry software was the most common (62.8% [27/43]).Although 52.1% (38/73) assumed that AI is currently useful in practice, 86.3% (63/73) expected it to be useful for clinical practice within 10 years.The main expected benefits were reducing the time spent on repetitive tasks (91.8% [67/73]) and improving reading accuracy and reducing errors (72.6% [53/73]).Those who experienced AI software were more familiar with AI (adjusted odds ratio, 7.1 [95% confidence interval, 1.81-27.81];P = 0.005).More than half of the respondents with AI software experience (55.8% [24/43]) agreed that AI should be included in training curriculums, while almost all (95.3% [41/43]) believed that radiologists should coordinate to improve its performance.Conclusion: A majority of respondents experienced AI software and showed a proactive attitude toward adopting AI in clinical practice, suggesting that AI should be incorporated into training and active participation in AI development should be encouraged.
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Ten studies describe factors relating to AI's impact on clinical tasks, such as an overall decreased time spent on repetitive medical and non-medical tasks and the possibility to delegate other tasks to AI, which could allow more time and focus on direct interaction with patients or other perceived critical tasks.
“Ten studies describe factors relating to AI’s impact on their clinical tasks, such as an overall decreased time spent on repetitive medical and non-medical tasks [30, 40, 46, 52–55] and the possibility to delegate other tasks to AI [47], which in turn could allow them to focus more time and focus on direct interaction with their patients or other perceived critical tasks [45, 56].”
From Healthcare professionals’ perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels (2025) · cites this paper as [53]
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References 24 · Cited by 15
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- [1]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 30353365 PMC6199205
- [2]Chartrand G Cheng PM Vorontsov E Drozdzal M Turcotte S Pal CJ Deep learning: a primer for radiologists Radiographics 2017 37 2113 2131 29131760
- [3]Havaei M Guizard N Larochelle H Jodoin PM Deep learning trends for focal brain pathology segmentation in MRI Holzinger A Machine learning for health informatics: state-of-the-art and future challenges Cham Springer 2016 125 148
- [4]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 26955021
- [5]Zhou LQ Wang JY Yu SY Wu GG Wei Q Deng YB Artificial intelligence in medical imaging of the liver World J Gastroenterol 2019 25 672 682 30783371 PMC6378542
- [6]Hwang EJ Goo JM Yoon SH Beck KS Seo JB Choi BW 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 34564966 PMC8546139
- [7]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 35762186 PMC9434734
- [8]Sakai K Yamada K Machine learning studies on major brain diseases: 5-year trends of 2014-2018 Jpn J Radiol 2019 37 34 72 30498877
- [9]Olthof AW van Ooijen PMA Rezazade Mehrizi MH Promises of artificial intelligence in neuroradiology: a systematic technographic review Neuroradiology 2020 62 1265 1278 32318774 PMC7479016
- [10]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 33856519 PMC8128724
- [12]Persson K Barca ML Cavallin L Brækhus A Knapskog AB Selbæk G 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 29172642
- [13]Sheth SA Lopez-Rivera V Barman A Grotta JC Yoo AJ Lee S Machine learning–enabled automated determination of acute ischemic core from computed tomography angiography Stroke 2019 50 3093 3100 31547796
- [15]Yahav-Dovrat A Saban M Merhav G Lankri I Abergel E Eran A Evaluation of artificial intelligence-powered identification of large-vessel occlusions in a comprehensive stroke center AJNR Am J Neuroradiol 2021 42 247 254 33384294 PMC7872164
- [17]Gallix B Chong J Artificial intelligence in radiology: who’s afraid of the big bad wolf? Eur Radiol 2019 29 1637 1639 30734848
- [18]Huisman M Ranschaert E Parker W Mastrodicasa D Koci M Pinto de Santos D 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 33744991 PMC8379099
- [19]Coppola F Faggioni L Regge D Giovagnoni A Golfieri R Bibbolino C Artificial intelligence: radiologists’ expectations and opinions gleaned from a nationwide online survey Radiol Med 2021 126 63 71 32350797
- [20]Gong B Nugent JP Guest W Parker W Chang PJ Khosa F Influence of artificial intelligence on Canadian medical students’ preference for radiology specialty: ANational survey study Acad Radiol 2019 26 566 577 30424998
- [21]Scheetz J Rothschild P McGuinness M Hadoux X Soyer HP Janda M A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology Sci Rep 2021 11 5193 33664367 PMC7933437
- [22]Shin SY Current status and future direction of digital health in Korea Korean J Physiol Pharmacol 2019 23 311 315 31496868 PMC6717792
- [24]Perneger TV What’s wrong with Bonferroni adjustments BMJ 1998 316 1236 1238 9553006 PMC1112991
- [26]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 31673823 PMC6823335
- Authors
- Choi, H., Sunwoo, L., Cho, S. J., Baik, S. H., Bae, Y. J., Choi, B. S., Jung, C., & Kim, J. H.
- Affiliations
- Seoul National University (KR)
- Corresponding author
- Sunwoo, Leonard
- ORCID
- Venue
- Korean Journal of Radiology
- Publisher
- XMLink
- Published
- 2023
- Volume
- 24
- Issue
- 5
- Pages
- 454
- ISSN
- 1229-6929 (ISSN-L)
- 2005-8330
- PMC
- PMC10157324
- OpenAlex
- W4366504065
- Cited by
- 15
- References
- 24
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 4
- FWCI
- 0.60
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Korean Journal of Radiology (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Seoul National University Bundang Hospital (09-2019-006)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence, neuroradiology, artificial intelligence adoption, Korea, clinical workflow efficiency
- MeSH terms
- Radiologists, Artificial Intelligence, Humans, Surveys and Questionnaires, Software, Internet, Republic of Korea
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