National Academy of Agricultural Sciences (NAAS)
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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcmas@gmail.com / submit@ijcmas.com Editor-in-chief: Dr.M.Prakash Index Copernicus ICV 2018: 95.39 NAAS RATING 2020: 5.38 |
Phenomenal developments in Artificial Intelligence/ Machine Learning (AI/ML) have led to the creation of powerful computerized algorithms with proven capabilities in the performance of some tasks in the radiology workflow. Predictions of the impact that AI/ML will have in the field of Diagnostic Radiology (DR) range from rendering radiologists obsolete to drastic changes in its practice. This has resulted in varied attitudes and perceptions of AI among radiologists and radiology residents. It is, therefore, key that radiologists be well versed with terminologies, concepts, and applications of AI/ML in DR to enable them to accurately project their potential effects and prepare them for the same. This study assessed the knowledge, attitudes, and practice of radiologists and radiology residents towards AI/ML in the field of DR in Kenya. A cross-sectional descriptive study method was used. The study was conducted among members of the Kenya Association of Radiologists (KAR). Eligible persons included radiologists and radiology residents based in Kenya. Data was collected by sharing a web-based questionnaire on the association’s WhatsApp platform, which had a membership of 199. Total sampling technique was used. Study variables were be calculated by the use of percentages and frequencies. Pearson’s Chi-square and Mann-Whitney U test were utilized to compare categorical data and study groups, respectively. This study is of help in identifying the level of knowledge of AI in DR, its utilization in daily practice, and the prevailing attitudes and perceptions surrounding it. The data was analysed using Statistical Package for Social Sciences (SPSS) version 26. A considerable majority of the participants had basic knowledge on Artificial intelligence, for they had read/watched/attended an AI presentation (n = 73, 65.8%). Less than half of the participants were knowledgeable on machine learning, artificial neural networks and deep learning concept. The use of AI in detection in radiology emerged as the most mentioned application (37.4%), with the remaining applications such as segmentation, speech recognition, registration, workflow management, protocol optimization and others only accounting for less than 20% individually. Utilization of AI application in daily radiology practice was scarce, with only 12.6% utilizing AI. Slightly more than two-thirds (68.5%) felt that the future practice of radiology would change as a result of AI. Nearly half of the participants felt that AI/ML application has both positive and negative effect on the field of radiology (44.1%), while the rest considered IA/ML as holding the potential to make radiology exciting and good (55.9%). Approximately two-thirds of the participants indicated their willingness to be involved in the process of development and training of ML algorithms so that they can do some of the tasks that a radiologist does (67.6%). At least 64% of the participants indicated that they had read an article on AI application in radiology. Around two-thirds of the participant felt that the current knowledge on AI applications has no bearing on their decision to pursue a career as a radiologist (61.3%). The results from this study show that consultant radiologists and radiology residents have a basic knowledge of AI while lacking knowledge on related concepts. Consultant radiologists and residents generally have a positive attitude towards AI application in Radiology. The utilization of AI applications in daily radiology practice in Kenya is still low. To bridge the knowledge gap, a course on AI/ML applications in Radiology should be introduced to the residency program while continuous medical education should be provided to radiologists.
Abuzaid M M, Elshami W, Tekin H, Issa B. Assessment of the Willingness of Radiologists and Radiographers to Accept the Integration of Artificial Intelligence Into Radiology Practice. Acad Radiol. 2020;(7):1–8. http://dx.doi.org/10.1016/j.acra.2020.09.014
Bin Dahmash A, Alabdulkareem M, Alfutais A, Kamel A M, Alkholaiwi F, Alshehri S, et al., Artificial intelligence in radiology: does it impact medical students preference for radiology as their future career? BJR|Open. 2020;2(1):20200037. https://doi.org/10.1259/bjro.20200037
Botwe B O, Akudjedu T N, Antwi W K, Rockson P, Mkoloma S S, Balogun E O, et al., The integration of artificial intelligence in medical imaging practice: Perspectives of African radiographers. Radiography. 2021;(February):0–6. http://dx.doi.org/10.1016/j.radi.2021.01.008
Chockley K, Emanuel E. The End of Radiology? Three Threats to the Future Practice of Radiology. J Am Coll Radiol [Internet]. 2016;13(12):1415–20. https://doi.org/10.1016/j.jacr.2016.07.010
Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Pianykh OS, et al., Current applications and future impact of machine learning in radiology. Vol. 288, Radiology. Radiological Society of North America Inc.; 2018. p. 318–28. https://doi.org/10.1148/radiol.2018171820
Codari M, Melazzini L, Morozov S P, van Kuijk C C, Sconfienza L M, Sardanelli F. Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology. Insights Imaging [Internet]. 2019 Dec 1 [cited 2020 Nov 8];10(1):105. https://doi.org/10.1186/s13244-019-0798-3
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 Medica. 2021;126(1):63–71. https://doi.org/10.1007/s11547-020-01205-y
El Naqa I, Haider M A, Giger M L, Ten Haken R K. Artificial intelligence: Reshaping the practice of radiological sciences in the 21st century. Br J Radiol. 2020;93(1106). https://doi.org/10.1259/bjr.20190855
Gong B, Nugent J P, Guest W, Parker W, Chang P J, Khosa F, et al., Influence of Artificial Intelligence on Canadian Medical Students’ Preference for Radiology Specialty: A National Survey Study. Acad Radiol [Internet]. 2019 Apr 1 [cited 2020 Nov 8];26(4):566–77. https://doi.org/10.1016/j.acra.2018.10.007
Hosny A, Parmar C, Quackenbush J, Schwartz L H, Aerts H J W L. Artificial intelligence in radiology. Vol. 18, Nature Reviews Cancer. Nature Publishing Group; 2018. p. 500–10. https://doi.org/10.1038/s41568-018-0016-5
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 https://doi.org/10.1007/s00330-021-07781-5
Idowu B M and Okedere T A. Diagnostic Radiology in Nigeria: A Country Report. J Glob Radiol. 2020;6(1). https://doi.org/10.7191/jgr.2020.1072
Idowu B M. Postgraduate radiology education in Nigeria: Looking backward and forward. South African J Radiol. 2018;22(1):1–7.
Langlotz CP. Will Artificial Intelligence Replace Radiologists? Radiol Artif Intell [Internet]. 2019 May 15 [cited 2020 Nov 8];1(3):e190058. https://doi.org/10.1148/ryai.2019190058
Letourneau-Guillon L, Camirand D, Guilbert F, Forghani R. Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics. Neuroimaging Clin N Am. 2020;30(4):e1–15. https://doi.org/10.1016/j.nic.2020.08.008
Liew C. The future of radiology augmented with Artificial Intelligence: A strategy for success. Vol. 102, European Journal of Radiology. Elsevier Ireland Ltd; 2018. p. 152–6. https://doi.org/10.1016/j.ejrad.2018.03.019
Lippert K A N. Artificial Intelligence in Radiology?: Promises and Pitfalls attitudes Artificial Intelligence in Radiology?: Promises and Pitfalls. 2020;(June).
Liu J, Kong X, Xia F, Bai X, Wang L, Qing Q, et al., Artificial intelligence in the 21st century. IEEE Access. 2018 https://doi.org/10.1109/ACCESS.2018.2819688
Martín Noguerol T, Paulano-Godino F, Martín-Valdivia MT, Menias CO, Luna A. Strengths, Weaknesses, Opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology. J Am Coll Radiol. 2019;16(9):1239–47. https://doi.org/10.1016/j.jacr.2019.05.047
Mollura DJ, Culp MP, Pollack E, Battino G, Scheel JR, Mango VL, et al., Artificial intelligence in low- And middle-income countries: Innovating global health radiology. Radiology. 2020;297(3):513–20. https://doi.org/10.1148/radiol.2020201434
Oren O, Gersh BJ, Bhatt DL. Artificial intelligence in medical imaging: switching from radiographic pathological data to clinically meaningful endpoints. Lancet Digit Heal. 2020;2(9):e486–8. https://doi.org/10.1016/s2589-7500(20)30160-6
Panesar A. Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes. Mach Learn AI Healthc Big Data Improv Heal Outcomes. 2019;1–368. http://dx.doi.org/10.1007/978-1-4842-3799-1
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 [Internet]. 2019 Apr 1 [cited 2020 Nov 8];29(4):1640–6. https://doi.org/10.1007/s00330-018-5601-1
Robinson. Artificial intelligence in healthcare; its knowledge, practice, and perception among medical personnel in the developing economy. J Radiat Med Trop. 2020;1(1):13.
Sit C, Srinivasan R, Amlani A, Muthuswamy K, Azam A, Monzon L, et al., Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights Imaging [Internet]. 2020 Dec 1 [cited 2020 Nov 8];11(1):14. https://doi.org/10.1186/s13244-019-0830-7
Tajaldeen A, Alghamdi S. Evaluation of radiologist’s knowledge about the Artificial Intelligence in diagnostic radiology: a survey-based study. Acta Radiol Open [Internet]. 2020 Jul 31 [cited 2020 Nov 8];9(7):2058460120945320. https://doi.org/10.1177/2058460120945320
Tang A, Tam R, Cadrin-Chênevert A, Guest W, Chong J, Barfett J, et al., Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology. Vol. 69, Canadian Association of Radiologists Journal. Canadian Medical Association; 2018. p. 120–35. https://doi.org/10.1016/j.carj.2018.02.002
Wang S, Summers R M. Machine learning and radiology. Vol. 16, Medical Image Analysis. Elsevier; 2012. p. 933–51. https://doi.org/10.1016/j.media.2012.02.005
Waymel Q, Badr S, Demondion X, Cotten A, Jacques T. Impact of the rise of artificial intelligence in radiology: What do radiologists think? Diagn Interv Imaging. 2019;100(6):327–36. https://doi.org/10.1016/j.diii.2019.03.015
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