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Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS)

Author

Listed:
  • Wenjuan Fan

    (Hefei University of Technology
    Key Laboratory of Minister of Education on Process Optimization and Intelligent Decision-making)

  • Jingnan Liu

    (Hefei University of Technology)

  • Shuwan Zhu

    (Hefei University of Technology)

  • Panos M. Pardalos

    (University of Florida)

Abstract

Compared to the booming industry of AIMDSS, the usage of AIMDSS among healthcare professionals is relatively low in the hospital. Thus, a research on the acceptance and adoption intention of AIMDSS by health professionals is imperative. In this study, an integration of Unified theory of user acceptance of technology and trust theory is proposed for exploring the adoption of AIMDSS. Besides, two groups of additional factors, related to AIMDSS (task complexity, technology characteristics, and perceived substitution crisis) and health professionals’ characteristics (propensity to trust and personal innovativeness in IT) are considered in the integrated model. The data set of proposed research model is collected through paper survey and Internet survey in China. The empirical examination demonstrates a high predictive power of this proposed model in explaining AIMDSS adoption. Finally, the theoretical contribution and practical implications of this research are discussed.

Suggested Citation

  • Wenjuan Fan & Jingnan Liu & Shuwan Zhu & Panos M. Pardalos, 2020. "Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS)," Annals of Operations Research, Springer, vol. 294(1), pages 567-592, November.
    • Handle: RePEc:spr:annopr:v:294:y:2020:i:1:d:10.1007_s10479-018-2818-y
    DOI: 10.1007/s10479-018-2818-y
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    References listed on IDEAS

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    Cited by:

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    2. Wu, Min & Wang, Nanxi & Yuen, Kum Fai, 2023. "Can autonomy level and anthropomorphic characteristics affect public acceptance and trust towards shared autonomous vehicles?," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    3. Omrani, Nessrine & Rivieccio, Giorgia & Fiore, Ugo & Schiavone, Francesco & Agreda, Sergio Garcia, 2022. "To trust or not to trust? An assessment of trust in AI-based systems: Concerns, ethics and contexts," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    4. Mengting Cheng & Xianmiao Li & Jicheng Xu, 2022. "Promoting Healthcare Workers’ Adoption Intention of Artificial-Intelligence-Assisted Diagnosis and Treatment: The Chain Mediation of Social Influence and Human–Computer Trust," IJERPH, MDPI, vol. 19(20), pages 1-19, October.
    5. Rongbin Yang & Santoso Wibowo, 2022. "User trust in artificial intelligence: A comprehensive conceptual framework," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2053-2077, December.
    6. Ariel K. H. Lui & Maggie C. M. Lee & Eric W. T. Ngai, 2022. "Impact of artificial intelligence investment on firm value," Annals of Operations Research, Springer, vol. 308(1), pages 373-388, January.

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