| Peer-Reviewed

Exploring the Knowledge, Attitudes, and Practices of Radiographers Regarding the Use of Artificial Intelligence in CT in Selected Private Hospitals in KZN

Received: 17 August 2022     Accepted: 15 September 2022     Published: 30 November 2022
Views:       Downloads:
Abstract

Artificial Intelligence (AI) has become increasingly important to daily lives. AI has introduced several algorithms in Computed Tomography (CT) which allow for improved image quality at a low dose. These systems execute tasks that are normally done by a human (Radiographers). Hence Radiographers need to have adequate knowledge of these AI applications. Previous studies reveal that Radiographers lack knowledge of the AI and its algorithms that are used in CT, which has been identified as a problem because limited information is passed on to students and trainees. The aim of this study was to explore Radiographers’ knowledge, attitudes, and practices toward the use of AI in CT. The research was conducted in selected private hospitals in Kwa-Zulu Natal in which semi-structured and in-depth face to face interviews using open-ended questions were used to collect data from 10 participants. Three main themes generated from the study’s theoretical framework were used for data analysis, namely knowledge, attitudes, and practices. Findings in this study indicate that Radiographers lack knowledge of AI and its algorithms that are used in CT. Their lack of knowledge is a result of a lack of training and education. Findings also suggest that a lack of knowledge contributes to uncertainty about the potential impact of AI implementation. However, Radiographers demonstrated interest in wanting to gain more information. Radiographers that participated in this study demonstrated a lack of knowledge, but also an interest in learning more about AI. This, therefore, necessitates collaboration between educational institutes and professional organizations to develop structured training programs for Radiographers.

Published in Radiation Science and Technology (Volume 8, Issue 4)
DOI 10.11648/j.rst.20220804.12
Page(s) 58-63
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Artificial Intelligence, Computed Tomography, Algorithms, Radiation, Radiographers

References
[1] McCollough, C. H. and Leng, S., 2020. Use of artificial intelligence in computed tomography dose optimization. Annals of the ICRP, 49 (1) 1: 113-125. Available at https://journals.sagepub.com/doi/pdf/10.1177/0146645320940827 (Accessed 5 July 2021).
[2] Zhang, Z and Seeram, E. 2020. The use of artificial intelligence in computed tomography image reconstruction - A literature review. Journal of medical imaging and radiation sciences, 51 (2020): 671-677. Available: https://www.jmirs.org/action/showPdf?pii=S1939-8654%2820%2930296-4 (Accessed 20 July 2021).
[3] Lee T. and Seeram E. 2020. The use of artificial intelligence in computed tomography image reconstruction: A systematic review. Radiology Open Journal 4 (2): 30-38. Available at: https://doi:10.17140/ROJ-4-129 (Accessed 5 July 2021).
[4] Hardy, M. and Harvey, H., 2020. Artificial intelligence in diagnostic imaging: impact on the radiography profession. The British journal of radiology, 93 (1108): 20-40. Available at https://doi.org/10.1259/bjr.20190840 (accessed 5 June 2021).
[5] Abuzaid, M. M., Elshami, W., Tekin, H. and Issa, B., 2020. Assessment of the Willingness of Radiologists and Radiographers to Accept the Integration of Artificial Intelligence into Radiology Practice. Academic Radiology. 1-10. Available at https://doi.org/10.1016/j.acra.2020.09.014 (Accessed 4 June 2021).
[6] Abuzaid, M. M., Elshami, W., McConnell, J. and Tekin, H. O., 2021. An extensive survey of Radiographers from the Middle East and India on artificial intelligence integration in radiology practice. Health and Technology, 11 (5); 1045-1050. Available at: https://doi.org/10.1007/s12553021-00583-1 (Accessed 15 October 2021).
[7] Tomlinson, M., Sikander, S., Skeen, S., Marlow, M., du Toit, S., and Eisner, M., 2020. Artificial intelligence, big data, and mHealth: the frontiers of the prevention of violence against children. Frontiers in artificial intelligence, 80-100. Available at: https://doi.org/10.3389/frai.2020.543305 (Accessed 4 June 2021).
[8] Van de Venter, R., 2018. Moving towards automated digitised image interpretation. Friend or foe? South African Radiographer, 56 (1): 7-10. Available at: https://journals.co.za/docserver/fulltext/saradio_v56_n1_a3.pdf?expires=1610204539&id=id&ac cname=58140&checksum=85DFB91B8D1A5578931E4F5EDB639EC3 (accessed 5 July 2021).
[9] Colvin, K. 2020. Artificial Intelligence and the Future of Radiography. EMJ Radiol. 1: 2325. Available at https://kindabooks.biz/cgi.php?view=the-future-of-radiology-and-artificialintelligence-the.pdf (accessed 5 January 2021).
[10] Alzghoul, B. I., & Abdullah, N. A. (2015). Pain Management Practices by Nurses: An Application of the Knowledge, Attitude, and Practices (KAP) Model. Global journal of health science, 8 (6), 154–160. https://doi.org/10.5539/gjhs.v8n6p154(Accessed 5 January 2021).
[11] Wan, T. T., Rav-Marathe, K. and Marathe, S., 2016. A systematic review of KAP-O framework for diabetes. Medical Research Archives, 3 (9). Available at: https://esmed.org/MRA/mra/article/view/483 (Accessed 5 January 2021).
[12] Mohajan, H. (2016): An Analysis of Knowledge Management for the Development of Global Health. Published in: American Journal of Social Science. 4 (4): 38-57. Available at: https://mpra.ub.uni-muenchen.de/82959/ (Accessed 5 January 2021).
[13] Wuni, A. R., Botwe, B. O., and Akudjedu, T. N., 2021. Impact of artificial intelligence on clinical radiography practice: Futuristic prospects in a low resource setting. Radiography, 27; 69-73. Available at: https://doi.org/10.1016/j.radi.2021.07.021 (Accessed 20 October 2021).
[14] Botwe, B. O., Akudjedu, T. N., Antwi, W. K., Rockson, P., Mkoloma, S. S., Balogun, E. O., Elshami, W., Bwambale, J., Barare, C., Mdletshe, S. and Yao, B., 2021. The integration of artificial intelligence in medical imaging practice: perspectives of African Radiographers. Radiography. https://doi.org/10.1016/j.radi.2021.01.008 (Accessed 4 June 2021).
[15] Botwe, B. O., Antwi, W. K., Arkoh, S. and Akudjedu, T. N., 2021. Radiographers’ perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study. Journal of Medical Radiation Sciences, 260–268. Available at: https://doi.org/10.1002/jmrs.460 (Accessed 20 October 2021).
[16] Sarwar, S., Dent, A., Faust, K., Richer, M., Djuric, U., Van Ommeren, R. and Diamandis, P., 2019. Physician perspectives on integration of artificial intelligence into diagnostic pathology. NPJ digital medicine, 2 (1): 1-7. Available at: https://doi.org/10.1038/s41746-019-0106-0 (Accessed 02 June 2021).
[17] Sit, C., Srinivasan, R., Amlani, A., Muthuswamy, K., Azam, A., Monzon, L. and Poon, D. S., 2020. Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights into imaging, 11 (1), 1-6. https://link.springer.com/article/10.1186/s13244-019-0830-7; (Accessed 3 June 2021).
[18] Ooi, G. S., Liew, C., Ting, D. S. W. and Lim, T. C. C. 2020. Artificial Intelligence: A Singapore Response. Annals of the Academy of Medicine, Singapore, 49 (4): 256-258. Available at https://www.annals.edu.sg/pdf/49VolNo4Apr2020/V49N4p256.pdf (accessed 2 June 2021).
[19] Malamateniou, C., Knapp, K. M., Pergola, M., Woznitza, N. and Hardy, M., 2021. Artificial intelligence in radiography: Where are we now and what does the future hold? Radiography, 27, 58-62. Available at: https://doi.org/10.1016/j.radi.2021.07.015 (accessed 2 June 2021).
Cite This Article
  • APA Style

    Nondumiso Praise Zuma, Timika Mewalall, Thandokuhle Emmanuel Khoza. (2022). Exploring the Knowledge, Attitudes, and Practices of Radiographers Regarding the Use of Artificial Intelligence in CT in Selected Private Hospitals in KZN. Radiation Science and Technology, 8(4), 58-63. https://doi.org/10.11648/j.rst.20220804.12

    Copy | Download

    ACS Style

    Nondumiso Praise Zuma; Timika Mewalall; Thandokuhle Emmanuel Khoza. Exploring the Knowledge, Attitudes, and Practices of Radiographers Regarding the Use of Artificial Intelligence in CT in Selected Private Hospitals in KZN. Radiat. Sci. Technol. 2022, 8(4), 58-63. doi: 10.11648/j.rst.20220804.12

    Copy | Download

    AMA Style

    Nondumiso Praise Zuma, Timika Mewalall, Thandokuhle Emmanuel Khoza. Exploring the Knowledge, Attitudes, and Practices of Radiographers Regarding the Use of Artificial Intelligence in CT in Selected Private Hospitals in KZN. Radiat Sci Technol. 2022;8(4):58-63. doi: 10.11648/j.rst.20220804.12

    Copy | Download

  • @article{10.11648/j.rst.20220804.12,
      author = {Nondumiso Praise Zuma and Timika Mewalall and Thandokuhle Emmanuel Khoza},
      title = {Exploring the Knowledge, Attitudes, and Practices of Radiographers Regarding the Use of Artificial Intelligence in CT in Selected Private Hospitals in KZN},
      journal = {Radiation Science and Technology},
      volume = {8},
      number = {4},
      pages = {58-63},
      doi = {10.11648/j.rst.20220804.12},
      url = {https://doi.org/10.11648/j.rst.20220804.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.rst.20220804.12},
      abstract = {Artificial Intelligence (AI) has become increasingly important to daily lives. AI has introduced several algorithms in Computed Tomography (CT) which allow for improved image quality at a low dose. These systems execute tasks that are normally done by a human (Radiographers). Hence Radiographers need to have adequate knowledge of these AI applications. Previous studies reveal that Radiographers lack knowledge of the AI and its algorithms that are used in CT, which has been identified as a problem because limited information is passed on to students and trainees. The aim of this study was to explore Radiographers’ knowledge, attitudes, and practices toward the use of AI in CT. The research was conducted in selected private hospitals in Kwa-Zulu Natal in which semi-structured and in-depth face to face interviews using open-ended questions were used to collect data from 10 participants. Three main themes generated from the study’s theoretical framework were used for data analysis, namely knowledge, attitudes, and practices. Findings in this study indicate that Radiographers lack knowledge of AI and its algorithms that are used in CT. Their lack of knowledge is a result of a lack of training and education. Findings also suggest that a lack of knowledge contributes to uncertainty about the potential impact of AI implementation. However, Radiographers demonstrated interest in wanting to gain more information. Radiographers that participated in this study demonstrated a lack of knowledge, but also an interest in learning more about AI. This, therefore, necessitates collaboration between educational institutes and professional organizations to develop structured training programs for Radiographers.},
     year = {2022}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Exploring the Knowledge, Attitudes, and Practices of Radiographers Regarding the Use of Artificial Intelligence in CT in Selected Private Hospitals in KZN
    AU  - Nondumiso Praise Zuma
    AU  - Timika Mewalall
    AU  - Thandokuhle Emmanuel Khoza
    Y1  - 2022/11/30
    PY  - 2022
    N1  - https://doi.org/10.11648/j.rst.20220804.12
    DO  - 10.11648/j.rst.20220804.12
    T2  - Radiation Science and Technology
    JF  - Radiation Science and Technology
    JO  - Radiation Science and Technology
    SP  - 58
    EP  - 63
    PB  - Science Publishing Group
    SN  - 2575-5943
    UR  - https://doi.org/10.11648/j.rst.20220804.12
    AB  - Artificial Intelligence (AI) has become increasingly important to daily lives. AI has introduced several algorithms in Computed Tomography (CT) which allow for improved image quality at a low dose. These systems execute tasks that are normally done by a human (Radiographers). Hence Radiographers need to have adequate knowledge of these AI applications. Previous studies reveal that Radiographers lack knowledge of the AI and its algorithms that are used in CT, which has been identified as a problem because limited information is passed on to students and trainees. The aim of this study was to explore Radiographers’ knowledge, attitudes, and practices toward the use of AI in CT. The research was conducted in selected private hospitals in Kwa-Zulu Natal in which semi-structured and in-depth face to face interviews using open-ended questions were used to collect data from 10 participants. Three main themes generated from the study’s theoretical framework were used for data analysis, namely knowledge, attitudes, and practices. Findings in this study indicate that Radiographers lack knowledge of AI and its algorithms that are used in CT. Their lack of knowledge is a result of a lack of training and education. Findings also suggest that a lack of knowledge contributes to uncertainty about the potential impact of AI implementation. However, Radiographers demonstrated interest in wanting to gain more information. Radiographers that participated in this study demonstrated a lack of knowledge, but also an interest in learning more about AI. This, therefore, necessitates collaboration between educational institutes and professional organizations to develop structured training programs for Radiographers.
    VL  - 8
    IS  - 4
    ER  - 

    Copy | Download

Author Information
  • Department of Radiography, Durban University of Technology, Durban, South Africa

  • Department of Radiography, Durban University of Technology, Durban, South Africa

  • Department of Radiography, Durban University of Technology, Durban, South Africa

  • Sections