Advertisement

Assessment of the Willingness of Radiologists and Radiographers to Accept the Integration of Artificial Intelligence Into Radiology Practice

Published:October 29, 2020DOI:https://doi.org/10.1016/j.acra.2020.09.014

      Rationale and Objectives

      This study aimed to investigate radiologists' and radiographers' knowledge, perception, readiness, and challenges regarding Artificial Intelligence (AI) integration into radiology practice.

      Materials and Methods

      An electronically distributed cross-sectional study was conducted among radiologists and radiographers in the United Arab Emirates. The questionnaire captured the participants' demographics, qualifications, professional experience, and postgraduate training. Their knowledge, perception, organisational readiness, and challenges regarding AI integration into radiology were examined.

      Results

      There was a significant lack of knowledge and appreciation of the integration of AI into radiology practice. Organisations are stepping toward building AI implementation strategies. The availability of appropriate training courses is the main challenge for both radiographers and radiologists.

      Conclusion

      The excitement of AI implementation into radiology practise was accompanied by a lack of knowledge and effort required to improve the user's appreciation of AI. The knowledge gap requires collaboration between educational institutes and professional bodies to develop structured training programs for radiologists and radiographers.

      Key Words

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Academic Radiology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Kulkarni S
        • Seneviratne N
        • Baig MS
        • et al.
        Artificial intelligence in medicine: where are we now?.
        Acad Radiol. 2020; 27: 62-70
        • Davenport T
        • Kalakota R.
        DIGITAL TECHNOLOGY The potential for artificial intelligence in healthcare.
        Futur Healthc J. 2019; 6: 94-102
        • Gore JC.
        Artificial intelligence in medical imaging.
        Magn Reson Imaging. 2020;
        • Hosny A
        • Parmar C
        • Quackenbush J
        • et al.
        Artificial intelligence in radiology.
        Nat Rev Cancer. 2018;
        • Cowling C.
        Global review of radiography.
        Radiography. 2013; 19: 90-91
      1. European Society of Radiology. What the increasing presence of AI means for radiographers | AI Blog [Internet]. 2019 [cited 2020 Jun 17]. Available from: https://ai.myesr.org/healthcare/what-the-increasing-presence-of-ai-means-for-radiographers/

      2. Johnson L. The role of the radiographer in computed tomography imaging. 2017;1–9.

        • 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
        • Jha S
        • Cook T.
        Artificial intelligence in radiology—-the state of the future.
        Acad Radiol. 2020; 27: 1-2
        • Liew C.
        The future of radiology augmented with Artificial Intelligence: A strategy for success.
        Eur J Radiol. 2018; 102: 152-156
        • Maskara R
        • Bhootra V
        • Thakkar D
        • et al.
        A study on the perception of medical professionals towards artificial intelligence.
        Int J Multidiscip Res Dev. 2017; 4: 34-39
        • Mazurowski MA.
        Artificial intelligence in radiology: some ethical considerations for radiologists and algorithm developers.
        Acad Radiol [Internet]. 2020; 27 (Available from): 127-129
        • Ooi S
        • Makmur A
        • Soon Y
        • et al.
        Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey.
        Singapore Med J. 2019; (November 2019): 1-22
        • Tang A
        • Tam R
        • Cadrin-Chênevert A
        • et al.
        Canadian Association of Radiologists White Paper on artificial intelligence in radiology.
        Can Assoc Radiol J. 2018; 69: 120-135
        • Abuzaid MM
        • Elshami W
        • McConnell J
        • et al.
        Changing the model of radiography practice in the UAE: A snapshot of a profession in transition.
        Radiography. 2020;
        • Elshami W
        • Abuzaid M
        • Piersson AD
        • et al.
        Occupational dose and radiation protection practice in uae: a retrospective cross-sectional cohort study (2002–2016).
        Radiat Prot Dosimetry. 2019; 187: 426-437
        • Sit C
        • Srinivasan R
        • Amlani A
        • et al.
        Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey.
        Insights Imaging. 2020; 11: 7-12