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Doctor Resistance of Artificial Intelligence in Healthcare

Author

Listed:
  • Asma Chaibi

    (FSEGT, University of El Manar, Mediterranean School of Business, South Mediterranean University, Tunisia)

  • Imed Zaiem

    (Faculty of Economics and Management of Nabeul, University of Carthage, Tunisia)

Abstract

Artificial intelligence (AI) has revolutionized healthcare by enhancing the quality of patient care. Despite its advantages, doctors are still reluctant to use AI in healthcare. Thus, the authors' main objective is to obtain an in-depth understanding of the barriers to doctors' adoption of AI in healthcare. The authors conducted semi-structured interviews with 11 doctors. Thematic analysis as chosen to identify patterns using QSR NVivo (version 12). The results showed that the barriers to AI adoption are lack of financial resources, need for special training, performance risk, perceived cost, technology dependency, need for human interaction, and fear of AI replacing human work.

Suggested Citation

  • Asma Chaibi & Imed Zaiem, 2022. "Doctor Resistance of Artificial Intelligence in Healthcare," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 17(1), pages 1-13, January.
    • Handle: RePEc:igg:jhisi0:v:17:y:2022:i:1:p:1-13
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJHISI.315618
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    References listed on IDEAS

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    1. Su-In Lee & Safiye Celik & Benjamin A. Logsdon & Scott M. Lundberg & Timothy J. Martins & Vivian G. Oehler & Elihu H. Estey & Chris P. Miller & Sylvia Chien & Jin Dai & Akanksha Saxena & C. Anthony Bl, 2018. "A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
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